April 2012

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CER Scan [Epub ahead of print]

1. Stat Med. 2012 Mar 22. doi: 10.1002/sim.5312. [Epub ahead of print]

Testing superiority at interim analyses in a non-inferiority trial. Joshua Chen YH, Chen C.
Merck Research Laboratories, Rahway, NJ, PA, USA. Joshua_chen@merck.com.

Shift in research and development strategy from developing follow-on or ‘me-too’ drugs to differentiated medical products with potentially better efficacy than the standard of care (e.g., first-in-class, best-in-class, and bio-betters) highlights the scientific and commercial interests in establishing superiority even when a non-inferiority design, adequately powered for a pre-specified non-inferiority margin, is appropriate for various reasons. In this paper, we propose a group sequential design to test superiority at interim analyses in a non-inferiority trial. We will test superiority at the interim analyses using conventional group sequential methods, and we may stop the study because of better efficacy. If the study fails to establish superior efficacy at the interim and final analyses, we will test the primary non-inferiority hypothesis at the final analysis at the nominal level without alpha adjustment. Whereas superiority/non-inferiority testing no longer has the hierarchical structure in which the rejection region for testing superiority is a subset of that for testing non-inferiority, the impact of repeated superiority tests on the false positive rate and statistical power for the primary non-inferiority test at the final analysis is essentially ignorable. For the commonly used O’Brien-Fleming type alpha-spending function, we show that the impact is extremely small based upon Brownian motion boundary-crossing properties. Numerical evaluation further supports the conclusion for other alpha-spending functions with a substantial amount of alpha being spent on the interim superiority tests. We use a clinical trial example to illustrate the proposed design.
Copyright © 2012 John Wiley & Sons, Ltd. Copyright
PMID: 22438208  [PubMed - as supplied by publisher]

LINK: http://onlinelibrary.wiley.com/doi/10.1002/sim.5312/abstract

2. Am J Epidemiol. 2012 Mar 6. [Epub ahead of print]

Risk Prediction Measures for Case-Cohort and Nested Case-Control Designs: An Application to Cardiovascular Disease. Ganna A, Reilly M, de Faire U, Pedersen N, Magnusson P, Ingelsson E.

Case-cohort and nested case-control designs are often used to select an appropriate subsample of individuals from prospective cohort studies. Despite the great attention that has been given to the calculation of association estimators, no formal methods have been described for estimating risk prediction measures from these 2 sampling designs. Using real data from the Swedish Twin Registry (2004-2009), the authors sampled unstratified and stratified (matched) case-cohort and nested case-control subsamples and compared them with the full cohort (as "gold standard"). The real biomarker (high density lipoprotein cholesterol) and simulated biomarkers (BIO1 and BIO2) were studied in terms of association with cardiovascular disease, individual risk of cardiovascular disease at 3 years, and main prediction metrics. Overall, stratification improved efficiency, with stratified case-cohort designs being comparable to matched nested case-control designs. Individual risks and prediction measures calculated by using case-cohort and nested case-control designs after appropriate reweighting could be assessed with good efficiency, except for the finely matched nested case-control design, where matching variables could not be included in the individual risk estimation. In conclusion, the authors have shown that case-cohort and nested case-control designs can be used in settings where the research aim is to evaluate the prediction ability of new markers and that matching strategies for nested case-control designs may lead to biased prediction measures.
PMID: 22396388  [PubMed - as supplied by publisher]

LINK: http://aje.oxfordjournals.org/content/175/7/715.long

3. Lifetime Data Anal. 2012 Mar 2. [Epub ahead of print]

Comparison of estimators in nested case-control studies with multiple outcomes. Støer NC, Samuelsen SO. Department of Mathematics, University of Oslo, P.O. Box 1053, 0316, Oslo, Norway, nathalcs@math.uio.no.
Reuse of controls in a nested case-control (NCC) study has not been considered feasible since the controls are matched to their respective cases. However, in the last decade or so, methods have been developed that break the matching and allow for analyses where the controls are no longer tied to their cases. These methods can be divided into two groups; weighted partial likelihood (WPL) methods and full maximum likelihood methods. The weights in the WPL can be estimated in different ways and four estimation procedures are discussed. In addition, we address modifications needed to accommodate left truncation. A full likelihood approach is also presented and we suggest an aggregation technique to decrease the computation time. Furthermore, we generalize calibration for case-cohort designs to NCC studies. We consider a competing risks situation and compare WPL, full likelihood and calibration through simulations and analyses on a real data example.
PMID: 22382602  [PubMed - as supplied by publisher]

LINK: http://www.springerlink.com/content/3101254836k737p4/

4. Stat Methods Med Res. 2012 Feb 23. [Epub ahead of print]

Consistent causal effect estimation under dual misspecification and implications for confounder selection procedures. Gruber S, van der Laan MJ. Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Kresge 820, Boston, MA, USA.

In a previously published article in this journal, Vansteeland et al. [Stat Methods Med Res. Epub ahead of print 12 November 2010. DOI: 10.1177/0962280210387717] address confounder selection in the context of causal effect estimation in observational studies. They discuss several selection strategies and propose a procedure whose performance is guided by the quality of the exposure effect estimator. The authors note that when a particular linearity condition is met, consistent estimation of the target parameter can be achieved even under dual misspecification of models for the association of confounders with exposure and outcome and demonstrate the performance of their procedure relative to other estimators when this condition holds. Our earlier published work on collaborative targeted minimum loss based learning provides a general theoretical framework for effective confounder selection that explains the findings of Vansteelandt et al. and underscores the appropriateness of their suggestions that a confounder selection procedure should be concerned with directly targeting the quality of the estimate and that desirable estimators produce valid confidence intervals and are robust to dual misspecification.
PMID: 22368176  [PubMed - as supplied by publisher]

LINK: http://smm.sagepub.com/content/early/2012/02/23/0962280212437451.long

5. Stat Med. 2012 Feb 24. doi: 10.1002/sim.4504. [Epub ahead of print]

Variance estimation for stratified propensity score estimators. Williamson EJ, Morley R, Lucas A, Carpenter JR. Centre for MEGA Epidemiology, School of Population Health, University of Melbourne, Melbourne, Australia; Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia. ewi@unimelb.edu.au.

Propensity score methods are increasingly used to estimate the effect of a treatment or exposure on an outcome in non-randomised studies. We focus on one such method, stratification on the propensity score, comparing it with the method of inverse-probability weighting by the propensity score. The propensity score-the conditional probability of receiving the treatment given observed covariates-is usually an unknown probability estimated from the data. Estimators for the variance of treatment effect estimates typically used in practice, however, do not take into account that the propensity score itself has been estimated from the data. By deriving the asymptotic marginal variance of the stratified estimate of treatment effect, correctly taking into account the estimation of the propensity score, we show that routinely used variance estimators are likely to produce confidence intervals that are too conservative when the propensity score model includes variables that predict (cause) the outcome, but only weakly predict the treatment. In contrast, a comparison with the analogous marginal variance for the inverse probability weighted (IPW) estimator shows that routinely used variance estimators for the IPW estimator are likely to produce confidence intervals that are almost always too conservative. Because exact calculation of the asymptotic marginal variance is likely to be complex, particularly for the stratified estimator, we suggest that bootstrap estimates of variance should be used in practice. Copyright © 2012 John Wiley & Sons, Ltd.
PMID: 22362427  [PubMed - as supplied by publisher]

LINK: http://onlinelibrary.wiley.com/doi/10.1002/sim.4504/abstract

6. Health Serv Res. 2012 Feb 21. doi: 10.1111/j.1475-6773.2012.01387.x. [Epub ahead of print]

Measuring Racial/Ethnic Disparities in Health Care: Methods and Practical Issues. Cook BL, McGuire TG, Zaslavsky AM. Department of Psychiatry, Center for Multicultural Mental Health Research, Harvard Medical School, Somerville, MA.

OBJECTIVE: To review methods of measuring racial/ethnic health care disparities. STUDY DESIGN: Identification and tracking of racial/ethnic disparities in health care will be advanced by application of a consistent definition and reliable empirical methods. We have proposed a definition of racial/ethnic health care disparities based in the Institute of Medicine’s (IOM) Unequal Treatment report, which defines disparities as all differences except those due to clinical need and preferences. After briefly summarizing the strengths and critiques of this definition, we review methods that have been used to implement it. We discuss practical issues that arise during implementation and expand these methods to identify sources of disparities. We also situate the focus on methods to measure racial/ethnic health care disparities (an endeavor predominant in the United States) within a larger international literature in health outcomes and health care inequality. EMPIRICAL APPLICATION: We compare different methods of implementing the IOM definition on measurement of disparities in any use of mental health care and mental health care expenditures using the 2004-2008 Medical Expenditure Panel Survey. CONCLUSION: Disparities analysts should be aware of multiple methods available to measure disparities and their differing assumptions. We prefer a method concordant with the IOM definition. © Health Research and Educational Trust.
PMID: 22353147  [PubMed - as supplied by publisher]

LINK: http://onlinelibrary.wiley.com/doi/10.1111/j.1475-6773.2012.01387.x/abstract

CER Scan [published within the last 30 days]

1. Emerg Themes Epidemiol. 2012 Mar 19;9(1):1. [Epub ahead of print]

Causal diagrams in systems epidemiology. Joffe M, Gambhir M, Chadeau-Hyam M, Vineis P.

Methods of diagrammatic modelling have been greatly developed in the past two decades. Outside the context of infectious diseases, systematic use of diagrams in epidemiology has been mainly confined to the analysis of a single link: that between a disease outcome and its proximal determinant(s). Transmitted causes ("causes of causes") tend not to be systematically analysed. The infectious disease epidemiology modelling tradition models the human population in its environment, typically with the exposure-health relationship and the determinants of exposure being considered at individual and group/ecological levels, respectively. Some properties of the resulting systems are quite general, and are seen in unrelated contexts such as biochemical pathways. Confining analysis to a single link misses the opportunity to discover such properties. The structure of a causal diagram is derived from knowledge about how the world works, as well as from statistical evidence. A single diagram can be used to
characterise a whole research area, not just a single analysis – although this depends on the degree of consistency of the causal relationships between different populations – and can therefore be used to integrate multiple datasets. Additional advantages of system-wide models include: the use of instrumental variables – now emerging as an important technique in epidemiology in the context of mendelian randomisation, but under-used in the exploitation of "natural experiments"; the explicit use of change models, which have advantages with respect to inferring causation; and in the detection and elucidation of feedback.
PMID: 22429606  [PubMed - as supplied by publisher]

Free Full Text: http://www.ete-online.com/content/pdf/1742-7622-9-1.pdf

2. Pharmacoepidemiol Drug Saf. 2012 Mar;21(3):241-45. doi: DOI: 10.1002/pds.2306.

Subtle issues in model specification and estimation of marginal structural models. Yang W, Joffe MM.

We review the concept of time-dependent confounding by using the example in paper “Comparative effectiveness of individual angiotensin receptor blockers on risk of mortality in patients with chronic heart failure” by Desai et al. and illustrate how to adjust for it by using inverse probability of treatment weighting through a simulated example. We discuss a few subtle issues that arise in specification of the model for treatment required to fit marginal structural models (MSMs) and in specification of the structural model for the outcome. We discuss the differences between the effects estimated in MSMs and intention-to-treat effects estimated in randomized trials, followed by an outline of some limitations of MSMs. Copyright © 2012 John Wiley & Sons, Ltd.

LINK: http://onlinelibrary.wiley.com/doi/10.1002/pds.2306/abstract

Comment on:
Pharmacoepidemiol Drug Saf. 2012 Mar;21(3):233-40. doi: 10.1002/pds.2175. Epub 2011 Jul 22.
Comparative effectiveness of individual angiotensin receptor blockers on risk of mortality in patients with chronic heart failure. Desai RJ, Ashton CM, Deswal A, Morgan RO, Mehta HB, Chen H, Aparasu RR, Johnson ML. Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.

OBJECTIVE: There is little evidence on comparative effectiveness of individual angiotensin receptor blockers (ARBs) in patients with chronic heart failure (CHF). This study compared four ARBs in reducing risk of mortality in clinical practice.
METHODS: A retrospective analysis was conducted on a national sample of patients diagnosed with CHF from 1 October 1996 to 30 September 2002 identified from Veterans Affairs electronic medical records, with supplemental clinical data obtained from chart review. After excluding patients with exposure to ARBs within the previous 6 months, four treatment groups were defined based on initial use of
candesartan, valsartan, losartan, and irbesartan between the index date (1 October 2000) and the study end date (30 September 2002). Time to death was measured concurrently during that period. A marginal structural model controlled for sociodemographic factors, comorbidities, comedications, disease severity (left ventricular ejection fraction), and potential time-varying confounding affected by previous treatment (hospitalization). Propensity scores derived from a multinomial logistic regression were used as inverse probability of treatment weights in a generalized estimating equation to estimate causal effects.
RESULTS: Among the 1536 patients identified on ARB therapy, irbesartan was most frequently used (55.21%), followed by losartan (21.74%), candesartan (15.23%), and valsartan (7.81%). When compared with losartan, after adjusting for time-varying hospitalization in marginal structural model, candesartan (OR=0.79, 95%CI=0.42-1.50), irbesartan (OR=1.17, 95%CI=0.72-1.90), and valsartan (OR=0.98, 95%CI=0.45-2.14) were found to have similar effectiveness in reducing mortality in CHF patients.
CONCLUSION: Effectiveness of ARBs in reducing mortality is similar in patients with CHF in everyday clinical practice. Copyright © 2011 John Wiley & Sons, Ltd.
PMID: 21786364  [PubMed - in process]

LINK: http://onlinelibrary.wiley.com/doi/10.1002/pds.2175/abstract

March 2012

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CER Scan [Epub ahead of print]

1. Stat Med. 2012 Feb 17. doi: 10.1002/sim.4510. [Epub ahead of print]

Longitudinal structural mixed models for the analysis of surgical trials with noncompliance. Sitlani CM, Heagerty PJ, Blood EA, Tosteson TD. Department of Biostatistics, University of Washington, F-600 Health Sciences Building, Box 357232, Seattle, WA 98195, USA; Cardiovascular Health Research Unit, University of Washington, 1730 Minor Ave, Suite 1360, Box 358085, Seattle, WA. csitlani@u.washington.edu.

Patient noncompliance complicates the analysis of many randomized trials seeking to evaluate the effect of surgical intervention as compared with a nonsurgical treatment. If selection for treatment depends on intermediate patient characteristics or outcomes, then ‘as-treated’ analyses may be biased for the estimation of causal effects. Therefore, the selection mechanism for treatment and/or compliance should be carefully considered when conducting analysis of surgical trials. We compare the performance of alternative methods when endogenous processes lead to patient crossover. We adopt an underlying longitudinal structural mixed model that is a natural example of a structural nested model. Likelihood-based methods are not typically used in this context; however, we show that standard linear mixed models will be valid under selection mechanisms that depend only on past covariate and outcome history. If there are underlying patient characteristics that influence selection, then likelihood methods can be extended via maximization of the joint likelihood of exposure and outcomes. Semi-parametric causal estimation methods such as marginal structural models, g-estimation, and instrumental variable approaches can also be valid, and we both review and evaluate their implementation in this setting. The assumptions required for valid estimation vary across approaches; thus, the choice of methods for analysis should be driven by which outcome and selection assumptions are plausible. Copyright © 2012 John Wiley & Sons, Ltd.
PMID: 22344923 [PubMed - as supplied by publisher]

Link: http://onlinelibrary.wiley.com/doi/10.1002/sim.4510/abstract;jsessionid=B8169932E2A812E3947828E1330A31D8.d02t04

2. Biometrics. 2012 Feb 2. doi: 10.1111/j.1541-0420.2011.01722.x. [Epub ahead of print]

Assessing Treatment-Selection Markers using a Potential Outcomes Framework.
Huang Y, Gilbert PB, Janes H. Fred Hutchinson Cancer Research Center, Seattle, Washington, 98109, U.S.A. Department of Biostatistics, University of Washington, Seattle, WA

Summary Treatment-selection markers are biological molecules or patient characteristics associated with one’s response to treatment. They can be used to predict treatment effects for individual subjects and subsequently help deliver treatment to those most likely to benefit from it. Statistical tools are needed to evaluate a marker’s capacity to help with treatment selection. The commonly adopted criterion for a good treatment-selection marker has been the interaction between marker and treatment. While a strong interaction is important, it is, however, not sufficient for good marker performance. In this article, we develop novel measures for assessing a continuous treatment-selection marker, based on a potential outcomes framework. Under a set of assumptions, we derive the optimal decision rule based on the marker to classify individuals according to treatment benefit, and characterize the marker’s performance using the corresponding classification accuracy as well as the overall distribution of the classifier. We develop a constrained maximum-likelihood method for estimation and testing in a randomized trial setting. Simulation studies are conducted to demonstrate the performance of our methods. Finally, we illustrate the methods using an HIV vaccine trial where we explore the value of the level of preexisting immunity to adenovirus serotype 5 for predicting a vaccine-induced increase in the risk of HIV acquisition. © 2012, The nternational Biometric Society.
PMID: 22299708 [PubMed - as supplied by publisher]

Link: http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2011.01722.x/abstract

CER Scan [published within the last 30 days]

1. Am J Epidemiol. 2012 Feb 1;175(3):210-7. Epub 2011 Dec 23.

Dealing with missing outcome data in randomized trials and observational studies.
Groenwold RH, Donders AR, Roes KC, Harrell FE Jr, Moons KG. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, the Netherlands. r.h.h.groenwold@umcutrecht.nl

Although missing outcome data are an important problem in randomized trials and observational studies, methods to address this issue can be difficult to apply. Using simulated data, the authors compared 3 methods to handle missing outcome data: 1) complete case analysis; 2) single imputation; and 3) multiple imputation (all 3 with and without covariate adjustment). Simulated scenarios focused on continuous or dichotomous missing outcome data from randomized trials or observational studies. When outcomes were missing at random, single and multiple imputations yielded unbiased estimates after covariate adjustment. Estimates obtained by complete case analysis with covariate adjustment were unbiased as well, with coverage close to 95%. When outcome data were missing not at random, all methods gave biased estimates, but handling missing outcome data by means of 1 of the 3 methods reduced bias compared with a complete case analysis without covariate adjustment. Complete case analysis with covariate adjustment and multiple imputation yield similar estimates in the event of missing outcome data, as long as the same predictors of missingness are included. Hence, complete case analysis with covariate adjustment can and should be used as the analysis of choice more often. Multiple imputation, in addition, can accommodate the missing-not-at-random scenario more flexibly, making it especially suited for sensitivity analyses.
PMID: 22262640 [PubMed - in process]

Link: http://aje.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=22262640

2. Stat Med. 2012 Feb 20;31(4):383-96. doi: 10.1002/sim.4453.

Hierarchical priors for bias parameters in Bayesian sensitivity analysis for unmeasured confounding. McCandless LC, Gustafson P, Levy AR, Richardson S.

Faculty of Health Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. mccandless@sfu.ca

Recent years have witnessed new innovation in Bayesian techniques to adjust for unmeasured confounding. A challenge with existing methods is that the user is often required to elicit prior distributions for high-dimensional parameters that model competing bias scenarios. This can render the methods unwieldy. In this paper, we propose a novel methodology to adjust for unmeasured confounding that derives default priors for bias parameters for observational studies with binary covariates. The confounding effects of measured and unmeasured variables are treated as exchangeable within a Bayesian framework. We model the joint distribution of covariates by using a log-linear model with pairwise interaction terms. Hierarchical priors constrain the magnitude and direction of bias parameters. An appealing property of the method is that the conditional distribution of the unmeasured confounder follows a logistic model, giving a simple equivalence with previously proposed methods. We apply the method in a data example from pharmacoepidemiology and explore the impact of different priors for bias parameters on the analysis results. Copyright © 2011 John Wiley & Sons, Ltd.
PMID: 22253142 [PubMed - in process]

Link: http://onlinelibrary.wiley.com/doi/10.1002/sim.4453/abstract

3. Am J Epidemiol. 2012 Mar 1;175(5):368-75. Epub 2012 Feb 3.

Bayesian posterior distributions without markov chains. Cole SR, Chu H, Greenland S, Hamra G, Richardson DB.

Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976-1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) = 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR = 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR = 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of
315 human immunodeficiency virus seroconverters (1984-1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC.
PMCID: PMC3282880 [Available on 2013/3/1] PMID: 22306565 [PubMed - in process]

Link: http://aje.oxfordjournals.org/content/175/5/368.long

4. Med Care. 2012 Feb;50(2):109-16.

A longitudinal examination of a pay-for-performance program for diabetes care: evidence from a natural experiment. Cheng SH, Lee TT, Chen CC. Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taiwan. shcheng@ntu.edu.tw

BACKGROUND: Numerous studies have examined the impacts of pay-for-performance programs, yet little is known about their long-term effects on health care expenses.
OBJECTIVES: This study aimed to examine the long-term effects of a pay-for-performance program for diabetes care on health care utilization and expenses.
METHODS: This study represents a nationwide population-based natural experiment with a 4-year follow-up period under a compulsory universal health insurance program in Taiwan. The intervention groups consisted of 20,934 patients enrolled in the program in 2005, and 9694 patients continuously participated in the program for 4 years. Two comparison groups were selected by propensity score matching from patients seen by the same group of physicians. Generalized estimating equations were used to estimate differences-in-differences models to examine the effects of the pay-for-performance program.
RESULTS: Patients enrolled in the pay-for-performance program underwent significantly more diabetes specific examinations and tests after enrollment; the differences between the intervention and comparison groups declined gradually over time but remained significant. Patients in the intervention groups had a significantly higher number of diabetes-related physician visits in only the first year after enrollment and had fewer diabetes-related hospitalizations in the follow-up period. Concerning overall health care expenses, patients in the intervention groups spent more than the comparison group in the first year; however, the continual enrollees spent significantly less than their counterparts in the subsequent years.
CONCLUSIONS: The program seemed to achieve its primary goal in improving health care and providing long-term cost benefits.
PMID: 22249920 [PubMed - in process]

Link: http://journals.lww.com/lww-medicalcare/pages/articleviewer.aspx?year=2012&issue=02000&article=00001&type=abstract

CER Scan [articles of interest published within the last 4 months]

1. Value in Health [Available online 8 November 2011] DOI: 10.1016/j.jval.2011.08.1740

Conducting Comparative Effectiveness Research on Medications: The Views of a Practicing Epidemiologist from the Other Washington. Bruce M. Psaty

No Abstract
Link: http://www.valueinhealthjournal.com/article/PIIS1098301511033274/abstract?rss=yes

2. Health Serv Outcomes Res Method. 2011; 11:95-114

Extending iterative matching methods: an approach to improving covariate balance that allows prioritisation. Ramsahai RR, Grieve R, Sekhon JS.

Comparative effectiveness studies can identify the causal effect of treatment if treatment is unconfounded with outcome conditional on a set of measured covariates. Matching aims to ensure that the covariate distributions are similar between treatment and control groups in the matched samples, and this should be done iteratively by checking and improving balance. However, an outstanding concern facing matching methods is how to prioritise competing improvements in balance across different covariates. We address this concern by developing a ‘loss function’ that an iterative matching method can minimise. Our ‘loss function’ is a transparent summary of covariate imbalance in a matched sample and follows general recommendations in prioritising balance amongst covariates. We illustrate this approach by extending Genetic Matching (GM), an automated approach to balance checking. We use the method to reanalyse a high profile comparative effectiveness study of right heart catheterisation. We find that our loss function improves covariate balance compared to a standard GM approach, and to matching on the published propensity score.

Link: http://www.springerlink.com/content/bl41h30008667400/

February 2012

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CER Scan [Epub ahead of print]

1. Am J Epidemiol. 2012 Jan 5. [Epub ahead of print]

Bias in Observational Studies of Prevalent Users: Lessons for Comparative Effectiveness Research From a Meta-Analysis of Statins. Danaei G, Tavakkoli M, Hernán MA.

Randomized clinical trials (RCTs) are usually the preferred strategy with which to generate evidence of comparative effectiveness, but conducting an RCT is not always feasible. Though observational studies and RCTs often provide comparable estimates, the questioning of observational analyses has recently intensified because of randomized-observational discrepancies regarding the effect of postmenopausal hormone replacement therapy on coronary heart disease. Reanalyses of observational data that excluded prevalent users of hormone replacement therapy led to attenuated discrepancies, which begs the question of whether exclusion of prevalent users should be generally recommended. In the current study, the authors evaluated the effect of excluding prevalent users of statins in a meta-analysis of observational studies of persons with cardiovascular disease. The pooled, multivariate-adjusted mortality hazard ratio for statin use was 0.77 (95% confidence interval (CI): 0.65, 0.91) in 4 studies that compared incident users with nonusers, 0.70 (95% CI: 0.64, 0.78) in 13 studies that compared a combination of prevalent and incident users with nonusers, and 0.54 (95% CI: 0.45, 0.66) in 13 studies that compared prevalent users with nonusers. The corresponding hazard ratio from 18 RCTs was 0.84 (95% CI: 0.77, 0.91). It appears that the greater the proportion of prevalent statin users in observational studies, the larger the discrepancy between observational and randomized estimates.
PMID:22223710

CER Scan [published within the last 30 days]

1. J Clin Epidemiol. 2012 Feb;65(2):132-7. Epub 2011 Aug 12.

The “best balance” allocation led to optimal balance in cluster-controlled trials. de Hoop E, Teerenstra S, van Gaal BG, Moerbeek M, Borm GF. Department of Epidemiology, Biostatistics and HTA, 133, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands.

OBJECTIVE: Balance of prognostic factors between treatment groups is desirable because it improves the accuracy, precision, and credibility of the results. In cluster-controlled trials, imbalance can easily occur by chance when the number of cluster is small. If all clusters are known at the start of the study, the “best balance” allocation method (BB) can be used to obtain optimal balance. This method will be compared with other allocation methods.
STUDY DESIGN AND SETTING: We carried out a simulation study to compare the balance obtained with BB, minimization, unrestricted randomization, and matching for four to 20 clusters and one to five categorical prognostic factors at cluster level.
RESULTS: BB resulted in a better balance than randomization in 13-100% of the situations, in 0-61% for minimization, and in 0-88% for matching. The superior performance of BB increased as the number of clusters and/or the number of factors increased.
CONCLUSION: BB results in a better balance of prognostic factors than randomization, minimization, stratification, and matching in most situations. Furthermore, BB cannot result in a worse balance of prognostic factors than the other methods. Copyright © 2012 Elsevier Inc. All rights reserved.
PMID: 21840173

2. Clin Pharmacol Ther. 2012 Feb;91(2):165-7. doi: 10.1038/clpt.2011.208.

Challenges in designing comparative-effectiveness trials for antidepressants. Leon AC. Departments of Psychiatry and Public Health, Weill Cornell Medical College, New York, New York, USA.

Comparative-effectiveness antidepressant trials offer promise to provide empirical evidence for clinicians choosing among interventions. Whether such trials posit superiority or noninferiority (NI) hypotheses, they pose formidable challenges. For instance, if meaningful antidepressant differences are seen in comparative-superiority trials, they will be small. NI hypothesis testing, on the other hand, requires an a priori NI margin and evidence of trial assay sensitivity. Either design demands unusually large samples, which could render such trials infeasible.
PMID: 22261683 [PubMed - in process]

FEBRUARY THEME: Selected Methods Manuscripts from the Pharmacoepidemiology and Drug Safety Mini-Sentinel Supplement
(Link to entire supplement: http://onlinelibrary.wiley.com/doi/10.1002/pds.v21.S1/issuetoc)

1. Pharmacoepidemiol Drug Saf. 2012 Jan;21 Suppl 1:1-8. doi: 10.1002/pds.2343.

The U.S. Food and Drug Administration’s Mini-Sentinel program: status and direction. Platt R, Carnahan RM, Brown JS, Chrischilles E, Curtis LH, Hennessy S, Nelson JC, Racoosin JA, Robb M, Schneeweiss S, Toh S, Weiner MG.

The Mini-Sentinel is a pilot program that is developing methods, tools, resources, policies, and procedures to facilitate the use of routinely collected electronic healthcare data to perform active surveillance of the safety of marketed medical products, including drugs, biologics, and medical devices. The U.S. Food and Drug Administration (FDA) initiated the program in 2009 as part of its Sentinel Initiative, in response to a Congressional mandate in the FDA Amendments Act of 2007. After two years, Mini-Sentinel includes 31 academic and private organizations. It has developed policies, procedures, and technical specifications for developing and operating a secure distributed data system comprised of separate data sets that conform to a common data model covering enrollment, demographics, encounters, diagnoses, procedures, and ambulatory dispensing of prescription drugs. The distributed data sets currently include administrative and claims data from 2000 to 2011 for over 300 million person-years, 2.4 billion encounters, 38 million inpatient hospitalizations, and 2.9 billion dispensings. Selected laboratory results and vital signs data recorded after 2005 are also available. There is an active data quality assessment and characterization program, and eligibility for medical care and pharmacy benefits is known. Systematic reviews of the literature have assessed the ability of administrative data to identify health outcomes of interest, and procedures have been developed and tested to obtain, abstract, and adjudicate full-text medical records to validate coded diagnoses. Mini-Sentinel has also created a taxonomy of study designs and analytical approaches for many commonly occurring situations, and it is developing new statistical and epidemiologic methods to address certain gaps in analytic capabilities. Assessments are performed by distributing computer programs that are executed locally by each data partner. The system is in active use by FDA, with the majority of assessments performed using customizable, reusable queries (programs). Prospective and retrospective assessments that use customized protocols are conducted as well. To date, several hundred unique programs have been distributed and executed. Current activities include active surveillance of several drugs and vaccines, expansion of the population, enhancement of the common data model to include additional types of data from electronic health records and registries, development of new methodologic capabilities, and assessment of methods to identify and validate additional health outcomes of interest. Copyright © 2012 John Wiley & Sons, Ltd.PMID: 22262586 [PubMed - in process]

Link to Free PDF: http://onlinelibrary.wiley.com/doi/10.1002/pds.2343/pdf

2. Pharmacoepidemiol Drug Saf. 2012 Jan;21 Suppl 1:18-22. doi: 10.1002/pds.2319.

A policy framework for public health uses of electronic health data. McGraw D, Rosati K, Evans B.

Successful implementation of a program of active safety surveillance of drugs and medical products depends on public trust. This article summarizes how the initial pilot phase of the FDA’s Sentinel Initiative, Mini-Sentinel, is being conducted in compliance with applicable federal and state laws. The article also sets forth the attributes of Mini-Sentinel that enhance privacy and public trust, including the use of a distributed data system (where identifiable information remains at the data partners) and the adoption by participants of additional mandatory policies and procedures implementing fair information practices. The authors conclude by discussing the implications of this model for other types of secondary health data uses. Copyright © 2012 John Wiley & Sons, Ltd.
PMID:22262589 [PubMed - in process]

Link to Free PDF: http://onlinelibrary.wiley.com/doi/10.1002/pds.2319/pdf

3. Pharmacoepidemiol Drug Saf. 2012 Jan;21 Suppl 1:23-31. doi: 10.1002/pds.2336.

Design considerations, architecture, and use of the Mini-Sentinel distributed data system. Curtis LH,Weiner MG, Boudreau DM, Cooper WO, Daniel GW, Nair VP, Raebel MA, Beaulieu NU, Rosofsky R, Woodworth TS, Brown JS.

Purpose: We describe the design, implementation, and use of a large, multiorganizational distributed database developed to support the Mini-Sentinel Pilot Program of the US Food and Drug Administration (FDA). As envisioned by the US FDA, this implementation will inform and facilitate the development of an active surveillance system for monitoring the safety of medical products (drugs, biologics, and devices) in the USA.
Methods: A common data model was designed to address the priorities of the Mini-Sentinel Pilot and to leverage the experience and data of participating organizations and data partners. A review of existing common data models informed the process. Each participating organization designed a process to extract, transform, and load its source data, applying the common data model to create the Mini-Sentinel Distributed Database. Transformed data were characterized and evaluated using a series of programs developed centrally and executed locally by participating organizations. A secure communications portal was designed to facilitate queries of the Mini-Sentinel Distributed Database and transfer of confidential data, analytic tools were developed to facilitate rapid response to common questions, and distributed querying software was implemented to facilitate rapid querying of summary data.
Results: As of July 2011, information on 99 260 976 health plan members was included in the Mini-Sentinel Distributed Database. The database includes 316 009 067 person-years of observation time, with members contributing, on average, 27.0 months of observation time. All data partners have successfully executed distributed code and returned findings to the Mini-Sentinel Operations Center.
Conclusion: This work demonstrates the feasibility of building a large, multiorganizational distributed data system in which organizations retain possession of their data that are used in an active surveillance system. Copyright © 2012 John Wiley & Sons, Ltd.
PMID: 22262590 [PubMed - in process]

Link to Free PDF: http://onlinelibrary.wiley.com/doi/10.1002/pds.2336/pdf

4. Pharmacoepidemiol Drug Saf. 2012 Jan;21 Suppl 1:41-49. doi: 10.1002/pds.2328.

Using high-dimensional propensity scores to automate confounding control in a distributed medical product safety surveillance system. Rassen JA, Schneeweiss S.

Distributed medical product safety monitoring systems such as the Sentinel System, to be developed as a part of Food and Drug Administration’s Sentinel Initiative, will require automation of large parts of the safety evaluation process to achieve the necessary speed and scale at reasonable cost without sacrificing validity. Although certain functions will require investigator intervention, confounding control is one area that can largely be automated. The high-dimensional propensity score (hd-PS) algorithm is one option for automated confounding control in longitudinal healthcare databases. In this article, we discuss the use of hd-PS for automating confounding control in sequential database cohort studies, as applied to safety monitoring systems. In particular, we discuss the robustness of the covariate selection process, the potential for over- or under-selection of variables including the possibilities of M-bias and Z-bias, the computation requirements, the practical considerations in a federated database network, and the cases where automated confounding adjustment may not function optimally. We also outline recent improvements to the algorithm and show how the algorithm has performed in several published studies. We conclude that despite certain limitations, hd-PS offers substantial advantages over non-automated alternatives in active product safety monitoring systems. Copyright © 2012 John Wiley & Sons, Ltd.
PMID: 22262592 [PubMed - in process]

Link to Free PDF: http://onlinelibrary.wiley.com/doi/10.1002/pds.2328/pdf

5. Pharmacoepidemiol Drug Saf. 2012 Jan;21 Suppl 1:50-61. doi: 10.1002/pds.2330.

When should case-only designs be used for safety monitoring of medical products? Maclure M, Fireman B, Nelson JC, Hua W, Shoaibi A, Paredes A, Madigan D.

Purpose: To assess case-only designs for surveillance with administrative databases.
Methods: We reviewed literature on two designs that are observational analogs to crossover experiments: the self-controlled case series (SCCS) and the case-crossover (CCO) design.
Results: SCCS views the ‘experiment’ prospectively, comparing outcome risks in windows with different exposures. CCO retrospectively compares exposure frequencies in case and control windows. The main strength of case-only designs is they entail self-controlled analyses that eliminate confounding and selection bias by time-invariant characteristics not recorded in healthcare databases. They also protect privacy and are computationally efficient, as they require fewer subjects and variables. They are better than cohort designs for investigating transient effects of accurately recorded preventive agents, for example, vaccines. They are problematic if timing of self-administration is sporadic and dissociated from dispensing times, for example, analgesics. They tend to have less exposure misclassification bias and time-varying confounding if exposures are brief. Standard SCCS designs are bidirectional (using time both before and after the first exposure event), so they are more susceptible than CCOs to reverse-causality bias, including immortal-time bias. This is true also for sequence symmetry analysis, a simplified SCCS. Unidirectional CCOs use only time before the outcome, so they are less affected by reverse causality but susceptible to exposure-trend bias. Modifications of SCCS and CCO partially deal with these biases. The head-to-head comparison of multiple products helps to control residual biases.
Conclusion: The case-only analyses of intermittent users complement the cohort analyses of prolonged users because their different biases compensate for one another. Copyright © 2012 John Wiley & Sons, Ltd.
PMID:22262593 [PubMed - in process

Link to Free PDF: http://onlinelibrary.wiley.com/doi/10.1002/pds.2330/pdf

6. Pharmacoepidemiol Drug Saf. 2012 Jan;21 Suppl 1:62-71. doi: 10.1002/pds.2324.

Challenges in the design and analysis of sequentially monitored postmarket safety surveillance evaluations using electronic observational health care data. Nelson JC, Cook AJ, Yu O, Dominguez C, Zhao S, Greene SK, Fireman BH, Jacobsen SJ, Weintraub ES, Jackson LA.

Purpose: Many challenges arise when conducting a sequentially monitored medical product safety surveillance evaluation using observational electronic data captured during routine care. We review existing sequential approaches for potential use in this setting, including a continuous sequential testing method that has been utilized within the Vaccine Safety Datalink (VSD) and group sequential methods, which are used widely in randomized clinical trials.
Methods: Using both simulated data and preliminary data from an ongoing VSD evaluation, we discuss key sequential design considerations, including sample size and duration of surveillance, shape of the signaling threshold, and frequency of interim testing.
Results and Conclusions: All designs control the overall Type 1 error rate across all tests performed, but each yields different tradeoffs between the probability and timing of true and false positive signals. Designs tailored to monitor efficacy outcomes in clinical trials have been well studied, but less consideration has been given to optimizing design choices for observational safety settings, where the hypotheses, population, prevalence and severity of the outcomes, implications of signaling, and costs of false positive and negative findings are very different. Analytic challenges include confounding, missing and partially accrued data, high misclassification rates for outcomes presumptively defined using diagnostic codes, and unpredictable changes in dynamically accessed data over time (e.g., differential product uptake). Many of these factors influence the variability of the adverse events under evaluation and, in turn, the probability of committing a Type 1 error. Thus, to ensure proper Type 1 error control, planned sequential thresholds should be adjusted over time to account for these issues. Copyright © 2012 John Wiley & Sons, Ltd.
PMID: 22262594 [PubMed - in process]

Link to Free PDF: http://onlinelibrary.wiley.com/doi/10.1002/pds.2324/pdf

7. Pharmacoepidemiol Drug Saf. 2012 Jan;21 Suppl 1:72-81. doi: 10.1002/pds.2320.

Statistical approaches to group sequential monitoring of postmarket safety surveillance data: current state of the art for use in the Mini-Sentinel pilot. Cook AJ, Tiwari RC, Wellman RD, Heckbert SR, Li L, Heagerty P, Marsh T, Nelson JC.

Purpose: This manuscript describes the current statistical methodology available for active postmarket surveillance of pre-specified safety outcomes using a prospective incident user concurrent control cohort design with existing electronic healthcare data.
Methods: Motivation of the active postmarket surveillance setting is provided using the Food and Drug Administration’s Mini-Sentinel Pilot as an example. Four sequential monitoring statistical methods are presented including the Lan–Demets error spending approach, a matched likelihood ratio test statistic approach with the binomial MaxSPRT as a special case, the conditional sequential sampling procedure with stratification, and a generalized estimating equation regression approach using permutation. Information on the assumptions, limitations, and advantages of each approach is provided, including how each method defines sequential monitoring boundaries, what test statistic is used, and how robust it is to settings of rare events or frequent testing.
Results: A hypothetical example of how the approaches could be applied to data comparing a medical product of interest, drug A, to a concurrent control drug, drug B, is presented including providing the type of information one would have available for monitoring such drugs. Copyright © 2012 John Wiley & Sons, Ltd.
PMID: 22262595 [PubMed - in process]

Link to Free PDF: http://onlinelibrary.wiley.com/doi/10.1002/pds.2320/pdf

8. Pharmacoepidemiol Drug Saf. 2012 Jan;21 Suppl 1:282-290. doi: 10.1002/pds.2337.

A protocol for active surveillance of acute myocardial infarction in association with the use of a new antidiabetic pharmaceutical agent. Fireman B, Toh S, Butler MG, Go AS, Joffe HV, Graham DJ, Nelson JC, Daniel GW, Selby JV.

Purpose: To describe a protocol for active surveillance of acute myocardial infarction (AMI) in users of a recently approved oral antidiabetic medication, saxagliptin, and to provide the rationale for decisions made in drafting the protocol.
Methods: A new-user cohort design is planned for evaluating data from at least four Mini-Sentinel data partners from 1 August 2009 (following US Food and Drug Administration’s approval of saxagliptin) through mid-2013. New users of saxagliptin will be compared in separate analyses with new users of sitagliptin, pioglitazone, long-acting insulins, and second-generation sulfonylureas. Two approaches to controlling for confounding will be evaluated: matching by exposure propensity score and stratification by AMI risk score. The primary analyses will use Cox regression models specified in a way that does not require pooling of patient-level data from the data partners. The Cox models are fit to summarized data on risk sets composed of saxagliptin users and similar comparator users at the time of an AMI. Secondary analyses will use alternative methods including Poisson regression and will explore whether further adjustment for covariates available only at some data partners (e.g., blood pressure) modifies results.
Results: The results of this study are pending.
Conclusions: The proposed protocol describes a design for surveillance to evaluate the safety of a newly marketed agent as postmarket experience accrues. It uses data from multiple partner organizations without requiring sharing of patient-level data and compares alternative approaches to controlling for confounding. It is hoped that this initial active surveillance project of the Mini-Sentinel will provide insights that inform future population-based surveillance of medical product safety. Copyright © 2012 John Wiley & Sons, Ltd.
PMID: 22262618 [PubMed - in process]

Link to Free PDF: http://onlinelibrary.wiley.com/doi/10.1002/pds.2337/pdf

January 2012

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CER Scan [Epub ahead of print]

1. Pharmacoepidemiol Drug Saf. 2011 Dec 8. doi: 10.1002/pds.2256. [Epub ahead of print]

Applying propensity scores estimated in a full cohort to adjust for confounding in subgroup analyses. Rassen JA, Glynn RJ, Rothman KJ, Setoguchi S, Schneeweiss S. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA. jrassen@post.harvard.edu.

BACKGROUND: A correctly specified propensity score (PS) estimated in a cohort (“cohort PS”) should, in expectation, remain valid in a subgroup population.
OBJECTIVE: We sought to determine whether using a cohort PS can be validly applied to subgroup analyses and, thus, add efficiency to studies with many subgroups or restricted data. METHODS: In each of three cohort studies, we estimated a cohort PS, defined five subgroups, and then estimated subgroup-specific PSs. We compared difference in treatment effect estimates for subgroup analyses adjusted by cohort PSs versus subgroup-specific PSs. Then, over 10 million times, we simulated a population with known characteristics of confounding, subgroup size, treatment interactions, and treatment effect and again assessed difference in point estimates. RESULTS: We observed that point estimates in most subgroups were substantially similar with the two methods of adjustment. In simulations, the effect estimates differed by a median of 3.4% (interquartile (IQ) range 1.3-10.0%). The IQ range exceeded 10% only in cases where the subgroup had < 1000 patients or few outcome events. CONCLUSIONS: Our empirical and simulation results indicated that using a cohort PS in subgroup analyses was a feasible approach, particularly in larger subgroups. Copyright © 2011 John Wiley & Sons, Ltd.
PMID: 22162077 [PubMed - as supplied by publisher]

2. Stat Methods Med Res. 2011 Nov 8. [Epub ahead of print]

Extension of the modified Poisson regression model to prospective studies with correlated binary data. Zou GY, Donner A. Department of Epidemiology & Biostatistics, and Robarts Clinical Trials of Robarts Research Institute, Schulich School of Medicine & Dentistry, Canada.

The Poisson regression model using a sandwich variance estimator has become a viable alternative to the logistic regression model for the analysis of prospective studies with independent binary outcomes. The primary advantage of this approach is that it readily provides covariate-adjusted risk ratios and associated standard errors. In this article, the model is extended to studies with correlated binary outcomes as arise in longitudinal or cluster randomization studies. The key step involves a cluster-level grouping strategy for the computation of the middle term in the sandwich estimator. For a single binary exposure variable without covariate adjustment, this approach results in risk ratio estimates and standard errors that are identical to those found in the survey sampling literature. Simulation results suggest that it is reliable for studies with correlated binary data, provided the total number of clusters is at least 50. Data from observational and cluster randomized studies are used to illustrate the methods.
PMID: 22072596 [PubMed - as supplied by publisher]

3. J Clin Psychopharmacol. 2011 Dec 22. [Epub ahead of print]

Treating Depression After Initial Treatment Failure: Directly Comparing Switch and Augmenting Strategies in STAR*D. Gaynes BN, Dusetzina SB, Ellis AR, Hansen RA, Farley JF, Miller WC, Stürmer T. Department of Psychiatry, School of Medicine, UNC at Chapel Hill, Chapel Hill, NC; Department of Health Care Policy, Harvard Medical School, Boston, MA; Harrison School of Pharmacy, Auburn University, Auburn, AL; Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, and Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC.

OBJECTIVE: Augmenting and switching antidepressant medications are the 2 most common next-step strategies for depressed patients failing initial medication treatment. These approaches have not been directly compared; thus, our objectives are to compare outcomes for medication augmentation versus switching for patients with major depressive disorder in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) clinical trial. METHODS: We conducted a retrospective analysis of participants aged 18 to 75 years with DSM-IV nonpsychotic depression who failed to remit with initial treatment in the STAR*D clinical trial (N =1292). We compared depressive symptom remission, response, and quality of life among participants in each study arm using propensity score matching to minimize selection bias. RESULTS: The propensity-score-matched augment (N = 269) and switch (N = 269) groups were well balanced on measured characteristics. Neither the likelihood of remission (risk ratio, 1.14; 95% confidence level, 0.82-1.58) or response (risk ratio, 1.14; 95% confidence level, 0.82-1.58), nor the time to remission (log-rank test, P = 0.946) or response (log-rank test, P = 0.243) differed by treatment strategy. Similarly, quality of life did not differ. Post hoc analyses suggested that augmentation improved outcomes for patients tolerating 12 or more weeks of initial treatment and those with partial initial treatment response. CONCLUSIONS: For patients receiving and tolerating aggressive initial antidepressant trials, there is no clear preference for next-step augmentation versus switching. Findings tentatively suggest that those who complete an initial treatment of 12 weeks or more and have a partial response with residual mild depressive severity may benefit more from augmentation relative to switching.
PMID: 22198447 [PubMed - as supplied by publisher]

4. J Clin Psychopharmacol. 2011 Dec 22. [Epub ahead of print]

Variation in Antipsychotic Treatment Choice Across US Nursing Homes. Huybrechts KF, Rothman KJ, Brookhart MA, Silliman RA, Crystal S, Gerhard T, Schneeweiss S. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School; Department of Epidemiology, Boston University School of Public Health, Boston, MA; RTI Health Solutions, Research Triangle Park; UNC, Gillings School of Global Public Health, Chapel Hill, NC; Department of Medicine, Boston University School of Medicine, Boston, MA; and Rutgers University, New Brunswick, NJ.

OBJECTIVE: Despite serious safety concerns, antipsychotic medications continue to be used widely in US nursing homes. The objective of this study was to quantify the variation in antipsychotic treatment choice across US nursing homes, and to characterize its correlates.
METHODS: Prescribing practices were assessed in a cohort of 65,618 patients 65 years or older in 45 states who initiated treatment with an antipsychotic medication after nursing home admission between 2001 and 2005, using merged Medicaid; Medicare; Minimum Data Set; and Online Survey, Certification, and Reporting data. We fit mixed-effects logistic regression models to examine how antipsychotic treatment choice at the patient-level depends on patient and nursing home fixed and random effects. RESULTS: Among antipsychotic medication users, 9% of patients initiated treatment with a conventional agent. After adjustment for case-mix and facility characteristics, 95% of nursing homes had a predicted conventional antipsychotic prescribing rate between 2% and 20%. Individually, patient characteristics accounted for 36% of the explained variation, facility characteristics for 23%, and nursing home prescribing tendency for 81%. Results were consistent in the subgroup of nursing home patients with a diagnosis of dementia. The prescribing physician was not considered as a determinant of treatment choice owing to data limitations.
CONCLUSION: These findings indicate that antipsychotic treatment choice is to some extent influenced by a nursing home’s underling prescribing “culture.” This culture may reveal strategies for targeting quality improvement interventions. In addition, these findings suggest that a nursing home’s tendency for specific antipsychotics merits further exploration as an instrumental variable for improved confounding adjustment in comparative effectiveness studies.
PMID: 22198446 [PubMed - as supplied by publisher]

5. Stat Med. 2011 Dec 4. doi: 10.1002/sim.4413. [Epub ahead of print]

Diagnosing imputation models by applying target analyses to posterior replicates of completed data. He Y, Zaslavsky AM. Department of Health Care Policy, Harvard Medical School, Boston, MA, 02115, USA. he@hcp.med.harvard.edu.

Multiple imputation fills in missing data with posterior predictive draws from imputation models. To assess the adequacy of imputation models, we can compare completed data with their replicates simulated under the imputation model. We apply analyses of substantive interest to both datasets and use posterior predictive checks of the differences of these estimates to quantify the evidence of model inadequacy. We can further integrate out the imputed missing data and their replicates over the completed-data analyses to reduce variance in the comparison. In many cases, the checking procedure can be easily implemented using standard imputation software by treating re-imputations under the model as posterior predictive replicates. Thus, it can be applied for non-Bayesian imputation methods. We also sketch several strategies for applying the method in the context of practical imputation analyses. We illustrate the method using two real data applications and study its property using a simulation. Copyright © 2011 John Wiley & Sons, Ltd.
PMID: 22139814 [PubMed - as supplied by publisher]

CER Scan [published within the last 30 days]

1. Epidemiology. 2012 Jan;23(1):151-8.

Is probabilistic bias analysis approximately bayesian? Maclehose RF, Gustafson P. From the Divisions of Biostatistics, and Epidemiology and Community Health, University of Minnesota, Minneapolis, MN; and Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada.

Case-control studies are particularly susceptible to differential exposure misclassification when exposure status is determined following incident case status. Probabilistic bias analysis methods have been developed as ways to adjust standard effect estimates based on the sensitivity and specificity of exposure misclassification. The iterative sampling method advocated in probabilistic bias analysis bears a distinct resemblance to a Bayesian adjustment; however, it is not identical. Furthermore, without a formal theoretical framework (Bayesian or frequentist), the results of a probabilistic bias analysis remain somewhat difficult to interpret. We describe, both theoretically and empirically, the extent to which probabilistic bias analysis can be viewed as approximately Bayesian. Although the differences between probabilistic bias analysis and Bayesian approaches to misclassification can be substantial, these situations often involve unrealistic prior specifications and are relatively easy to detect. Outside of these special cases, probabilistic bias analysis and Bayesian approaches to exposure misclassification in case-control studies appear to perform equally well.
PMID: 22157311 [PubMed - in process]

2. BMC Med Inform Decis Mak. 2011 Dec 14;11(1):75. [Epub ahead of print]

Evaluation of an automated safety surveillance system using risk adjusted Sequential Probability Ratio Testing. Matheny ME, Normand SL, Gross TP, Marinac-Dabic D, Loyo-Berrios N, Vidi VD, Donnelly S, Resnic FS.

BACKGROUND: Automated adverse outcome surveillance tools and methods have potential utility in quality improvement and medical product surveillance activities. Their use for assessing hospital performance on the basis of patient outcomes has received little attention. We compared risk-adjusted sequential probability ratio testing (RA-SPRT) implemented in an automated tool to Massachusetts public reports of 30-day mortality after isolated coronary artery bypass graft surgery. METHODS: A total of 23,020 isolated adult coronary artery bypass surgery admissions performed in Massachusetts hospitals between January 1, 2002 and September 30, 2007 were retrospectively re-evaluated. The RA-SPRT method was implemented within an automated surveillance tool to identify hospital outliers in yearly increments. We used an overall type I error rate of 0.05, an overall type II error rate of 0.10, and a threshold that signaled if the odds of dying 30-days after surgery was at least twice than expected. Annual hospital outlier status, based on the state-reported classification, was considered the gold standard. An event was defined as at least one occurrence of a higher-than-expected hospital mortality rate during a given year. RESULTS: We examined a total of 83 hospital-year observations. The RA-SPRT method alerted 6 events among three hospitals for 30-day mortality compared with 5 events among two hospitals using the state public reports, yielding a sensitivity of 100% (5/5) and specificity of 98.8% (79/80). CONCLUSIONS: The automated RA-SPRT method performed well, detecting all of the true institutional outliers with a small false positive alerting rate. Such a system could provide confidential automated notification to local institutions in advance of public reporting providing opportunities for earlier quality improvement interventions.
PMID: 22168892 [PubMed - as supplied by publisher]

Free Full Text: http://www.biomedcentral.com/content/pdf/1472-6947-11-75.pdf

3. Stat Med. 2011 Dec 20;30(29):3447-60. doi: 10.1002/sim.4355.

Gaussian-based routines to impute categorical variables in health surveys. Yucel RM, He Y, Zaslavsky AM. Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, SUNY, One University Place, Rensselaer, NY 12144-3456, USA. ryucel@albany.edu

The multivariate normal (MVN) distribution is arguably the most popular parametric model used in imputation and is available in most software packages (e.g., SAS PROC MI, R package norm). When it is applied to categorical variables as an approximation, practitioners often either apply simple rounding techniques for ordinal variables or create a distinct ‘missing’ category and/or disregard the nominal variable from the imputation phase. All of these practices can potentially lead to biased and/or uninterpretable inferences. In this work, we develop a new rounding methodology calibrated to preserve observed distributions to multiply impute missing categorical covariates. The major attractiveness of this method is its flexibility to use any ‘working’ imputation software, particularly those based on MVN, allowing practitioners to obtain usable imputations with small biases. A simulation study demonstrates the clear advantage of the proposed method in rounding ordinal variables and, in some scenarios, its plausibility in imputing nominal variables. We illustrate our methods on a widely used National Survey of Children with Special Health Care Needs where incomplete values on race posed a valid threat on inferences pertaining to disparities. Copyright © 2011 John Wiley & Sons, Ltd.
PMID: 21976366 [PubMed - in process]

JANUARY THEME: Applications of MSMs for Dealing with Time-varying Exposure

1. Int J Biostat. 2011;7(1):Article 34. Epub 2011 Sep 8.

Antihypertensive medication use and change in kidney function in elderly adults: a marginal structural model analysis. Odden MC, Tager IB, van der Laan MJ, Delaney JA, Peralta CA, Katz R, Sarnak MJ, Psaty BM, Shlipak MG. Oregon State University, USA.

BACKGROUND: The evidence for the effectiveness of antihypertensive medication use for slowing decline in kidney function in older persons is sparse. We addressed this research question by the application of novel methods in a marginal structural model.
METHODS: Change in kidney function was measured by two or more measures of cystatin C in 1,576 hypertensive participants in the Cardiovascular Health Study over 7 years of follow-up (1989-1997 in four U.S. communities). The exposure of interest was antihypertensive medication use. We used a novel estimator in a marginal structural model to account for bias due to confounding and informative censoring.
RESULTS: The mean annual decline in eGFR was 2.41 ± 4.91 mL/min/1.73 m(2). In unadjusted analysis, antihypertensive medication use was not associated with annual change in kidney function. Traditional multivariable regression did not substantially change these estimates. Based on a marginal structural analysis, persons on antihypertensives had slower declines in kidney function; participants had an estimated 0.88 (0.13, 1.63) ml/min/1.73 m(2) per year slower decline in eGFR compared with persons on no treatment. In a model that also accounted for bias due to informative censoring, the estimate for the treatment effect was 2.23
(-0.13, 4.59) ml/min/1.73 m(2) per year slower decline in eGFR.
CONCLUSION: In summary, estimates from a marginal structural model suggested that antihypertensive therapy was associated with preserved kidney function in hypertensive elderly adults. Confirmatory studies may provide power to determine the strength and validity of the findings.
PMCID: PMC3204667 [Available on 2012/9/8]
PMID: 22049266 [PubMed - in process]

2. Epidemiology. 2011 Nov;22(6):877-8.

Hormonal contraception and HIV risk: evaluating marginal-structural-model assumptions. Chen PL, Cole SR, Morrison CS.

Letter to the editor

PMID: 21968782 [PubMed - in process]

3. Pharmacoepidemiol Drug Saf. 2011 Jul 22. doi: 10.1002/pds.2175. [Epub ahead of print] Comparative effectiveness of individual angiotensin receptor blockers on risk of mortality in patients with chronic heart failure. Desai RJ, Ashton CM, Deswal A, Morgan RO, Mehta HB, Chen H, Aparasu RR, Johnson ML. Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.

 

OBJECTIVE: There is little evidence on comparative effectiveness of individual angiotensin receptor blockers (ARBs) in patients with chronic heart failure (CHF). This study compared four ARBs in reducing risk of mortality in clinical practice. METHODS: A retrospective analysis was conducted on a national sample of patients diagnosed with CHF from 1 October 1996 to 30 September 2002 identified from Veterans Affairs electronic medical records, with supplemental clinical data obtained from chart review. After excluding patients with exposure to ARBs within the previous 6 months, four treatment groups were defined based on initial use of candesartan, valsartan, losartan, and irbesartan between the index date (1 October 2000) and the study end date (30 September 2002). Time to death was measured concurrently during that period. A marginal structural model controlled for sociodemographic factors, comorbidities, comedications, disease severity (left ventricular ejection fraction), and potential time-varying confounding affected by previous treatment (hospitalization). Propensity scores derived from a multinomial logistic regression were used as inverse probability of treatment weights in a generalized estimating equation to estimate causal effects. RESULTS: Among the 1536 patients identified on ARB therapy, irbesartan was most frequently used (55.21%), followed by losartan (21.74%), candesartan (15.23%), and valsartan (7.81%). When compared with losartan, after adjusting for time-varying hospitalization in marginal structural model, candesartan (OR = 0.79, 95%CI = 0.42-1.50), irbesartan (OR = 1.17, 95%CI = 0.72-1.90), and valsartan (OR = 0.98, 95%CI = 0.45-2.14) were found to have similar effectiveness in reducing mortality in CHF patients. CONCLUSION: Effectiveness of ARBs in reducing mortality is similar in patients with CHF in everyday clinical practice. Copyright © 2011 John Wiley & Sons, Ltd.
PMID: 21786364 [PubMed - as supplied by publisher]

4. Clin Trials. 2011 Jun;8(3):277-87. doi: 10.1177/1740774511402526.

How to use marginal structural models in randomized trials to estimate the natural direct and indirect effects of therapies mediated by causal intermediates. Oba K, Sato T, Ogihara T, Saruta T, Nakao K. Translational Research and Clinical Trial Center, Hokkaido University Hospital, Hokkaido University, Japan. k.oba@huhp.hokudai.ac.jp

Erratum in
Clin Trials. 2011;8(5):680.

BACKGROUND: Although intention-to-treat analysis is a standard approach, additional supplemental analyses are often required to evaluate the biological relationship among interventions, intermediates, and outcomes. Therefore, we need to evaluate whether the effect of an intervention on a particular outcome is mediated by a hypothesized intermediate variable.
PURPOSE: To evaluate the size of the direct effect in the total effect, we applied the marginal structural model to estimate the average natural direct and indirect effects in a large-scale randomized controlled trial (RCT). Method The average natural direct effect is defined as the difference in the probability of a counterfactual outcome between the experimental and control arms, with the intermediate set to what it would have been, had the intervention been a control treatment. We considered two marginal structural models to estimate the average natural direct and indirect effects introduced by VanderWeele (Epidemiology 2009) and applied them in a large-scale RCT – the Candesartan Antihypertensive Survival
Evaluation in Japan (CASE-J trial) – that compared angiotensin receptor blockers and calcium-channel blockers in high-risk hypertensive patients.
RESULTS: There were no strong blood pressure-independent or dependent effects; however, a systolic blood pressure reduction of about 1.9  mmHg suppressed all events. Compared to the blood pressure-independent effects of calcium channel blockers, those of angiotensin receptor blockers contributed positively to cardiovascular and cardiac events, but negatively to cerebrovascular events.
LIMITATIONS: There is a particular condition for estimating the average natural direct effect. It is impossible to check whether this condition is satisfied with the available data.
CONCLUSION: We estimated the average natural direct and indirect effects through the achieved systolic blood pressure in the CASE-J trial. This first application of estimating the average natural effects in an RCT can be useful for obtaining an in-depth understanding of the results and further development of similar interventions.
PMID: 21730076 [PubMed - indexed for MEDLINE]

5. J Consult Clin Psychol. 2011 Apr;79(2):225-35. A marginal structural model analysis for loneliness: implications for intervention trials and clinical practice. VanderWeele TJ, Hawkley LC, Thisted RA, Cacioppo JT. Harvard University, Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. tvanderw@hsph.harvard.edu

 

OBJECTIVE: Clinical scientists, policymakers, and individuals must make decisions concerning effective interventions that address health-related issues. We use longitudinal data on loneliness and depressive symptoms and a new class of causal models to illustrate how empirical evidence can be used to inform intervention trial design and clinical practice.
METHOD: Data were obtained from a population-based study of non-Hispanic Caucasians, African Americans, and Latino Americans (N = 229) born between 1935 and 1952. Loneliness and depressive symptoms were measured with the UCLA Loneliness Scale-Revised and Center for Epidemiologic Studies Depression Scale, respectively. Marginal structural causal models were employed to evaluate the extent to which depressive symptoms depend not only on loneliness measured at a single point in time (as in prior studies of the effect of loneliness) but also on an individual’s entire loneliness history.
RESULTS: Our results indicate that if interventions to reduce loneliness by 1 standard deviation were made 1 and 2 years prior to assessing depressive symptoms, both would have an effect; together, they would result in an average reduction in depressive symptoms of 0.33 standard deviations, 95% CI [0.21,
0.44], p < .0001.
CONCLUSIONS: The magnitude and persistence of these effects suggest that greater effort should be devoted to developing practical interventions on alleviating loneliness and that doing so could be useful in the treatment and prevention of depressive symptoms. In light of the persistence of the effects of loneliness, our results also suggest that, in the evaluation of interventions on loneliness, it may be important to allow for a considerable follow-up period in assessing outcomes.
(c) 2011 APA, all rights reserved.
PMCID: PMC3079447 [Available on 2012/4/1]
PMID: 21443322 [PubMed - indexed for MEDLINE]

6. J Clin Psychopharmacol. 2011 Apr;31(2):226-30.

Differential 3-year effects of first- versus second-generation antipsychotics on subjective well-being in schizophrenia using marginal structural models. Lambert M, Schimmelmann BG, Schacht A, Suarez D, Haro JM, Novick D, Wagner T, Wehmeier PM, Huber CG, Hundemer HP, Dittmann RW, Naber D. Psychosis Centre, Department of Psychiatry and Psychotherapy, Centre for Psychosocial Medicine, University Medical Centre Hamburg-Eppendorf, Germany.

OBJECTIVE: This study examined the differential effects of first- (FGAs) versus second-generation antipsychotics (SGAs) on subjective well-being in patients with schizophrenia.
METHOD: Data were collected in an observational 3-year follow-up study of 2224 patients with schizophrenia. Subjective well-being was assessed with the Subjective Well-being under Neuroleptic Treatment Scale (SWN-K). Differential effects of FGAs versus SGAs were analyzed using marginal structural models in those patients taking antipsychotic monotherapy.
RESULTS: The marginal structural model, which analyzed the differential effect on the SWN-K total score, revealed that patients on SGAs had significantly higher SWN-K total scores, starting at 6 months (3.02 points; P = 0.0061, d = 0.20) and remaining significant thereafter (end point: 5.35 points; P = 0.0074, d = 0.36).
CONCLUSIONS: Results of this large observational study are consistent with a small but clinically relevant superiority of SGAs over FGAs in subjective well-being extending previous positive findings of differential effects on quality of life.
PMID: 21346606 [PubMed - indexed for MEDLINE]

7. Arch Intern Med. 2011 Jan 24;171(2):110-8. Epub 2010 Sep 27.

Similar outcomes with hemodialysis and peritoneal dialysis in patients with end-stage renal disease. Mehrotra R, Chiu YW, Kalantar-Zadeh K, Bargman J, Vonesh E. Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA 90502, USA. rmehrotra@labiomed.org

Comment in
Arch Intern Med. 2011 Jan 24;171(2):107-9.

BACKGROUND: The annual payer costs for patients treated with peritoneal dialysis (PD) are lower than with hemodialysis (HD), but in 2007, only 7% of dialysis patients in the United States were treated with PD. Since 1996, there has been no change in the first-year mortality of HD patients, but both short- and long-term outcomes of PD patients have improved.
METHODS: Data from the US Renal Data System were examined for secular trends in survival among patients treated with HD and PD on day 90 of end-stage renal disease (HD, 620 020 patients; PD, 64 406 patients) in three 3-year cohorts (1996-1998, 1999-2001, and 2002-2004) for up to 5 years of follow-up using a nonproportional hazards marginal structural model with inverse probability of treatment and censoring weighting.
RESULTS: There was a progressive attenuation in the higher risk for death seen in patients treated with PD in earlier cohorts; for the 2002-2004 cohort, there was no significant difference in the risk of death for HD and PD patients through 5 years of follow-up. The median life expectancy of HD and PD patients was 38.4 and 36.6 months, respectively. Analyses in 8 subgroups based on age (<65 and ≥65 years), diabetic status, and baseline comorbidity (none and ≥1) showed greater improvement in survival among patients treated with PD relative to HD at all follow-up periods.
CONCLUSION: In the most recent cohorts, patients who began treatment with HD or PD have similar outcomes.
PMID: 20876398 [PubMed - indexed for MEDLINE]

8. Epidemiology. 2010 Jul;21(4):528-39.

Estimating absolute risks in the presence of nonadherence: an application to a follow-up study with baseline randomization. Toh S, Hernández-Díaz S, Logan R, Robins JM, Hernán MA. Department of Epidemiology, Harvard School of Public Health, Boston, MA 02215

The intention-to-treat (ITT) analysis provides a valid test of the null hypothesis and naturally results in both absolute and relative measures of risk. However, this analytic approach may miss the occurrence of serious adverse effects that would have been detected under full adherence to the assigned treatment. Inverse probability weighting of marginal structural models has been used to adjust for nonadherence, but most studies have provided only relative measures of risk. In this study, we used inverse probability weighting to estimate both absolute and relative measures of risk of invasive breast cancer under full adherence to the assigned treatment in the Women’s Health Initiative estrogen-plus-progestin trial. In contrast to an ITT hazard ratio (HR) of 1.25 (95% confidence interval [CI] = 1.01 to 1.54), the HR for 8-year continuous estrogen-plus-progestin use versus no use was 1.68 (1.24 to 2.28). The estimated risk difference (cases/100 women) at year 8 was 0.83 (-0.03 to 1.69) in the ITT analysis, compared with 1.44 (0.52 to 2.37) in the adherence-adjusted analysis. Results were robust across various dose-response models. We also compared the dynamic treatment regimen “take hormone therapy until certain adverse events become apparent, then stop taking hormone therapy” with no use (HR = 1.64; 95% CI
= 1.24 to 2.18). The methods described here are also applicable to observational studies with time-varying treatments.
PMID: 20526200 [PubMed - indexed for MEDLINE]

9. Lifetime Data Anal. 2010 Jan;16(1):71-84. Epub 2009 Nov 6.

Relation between three classes of structural models for the effect of a time-varying exposure on survival. Young JG, Hernán MA, Picciotto S, Robins JM. Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Kresge Bldg Suite 820, Boston, MA 02115, USA. jyoung@hsph.harvard.edu

Standard methods for estimating the effect of a time-varying exposure on survival may be biased in the presence of time-dependent confounders themselves affected by prior exposure. This problem can be overcome by inverse probability weighted estimation of Marginal Structural Cox Models (Cox MSM), g-estimation of Structural Nested Accelerated Failure Time Models (SNAFTM) and g-estimation of
Structural Nested Cumulative Failure Time Models (SNCFTM). In this paper, we describe a data generation mechanism that approximately satisfies a Cox MSM, an SNAFTM and an SNCFTM. Besides providing a procedure for data simulation, our formal description of a data generation mechanism that satisfies all three models allows one to assess the relative advantages and disadvantages of each modeling approach. A simulation study is also presented to compare effect estimates across the three models.
PMID: 19894116 [PubMed - indexed for MEDLINE]

10. J Rheumatol. 2009 Mar;36(3):560-4. Epub 2009 Feb 4.

Prednisone, lupus activity, and permanent organ damage. Thamer M, Hernán MA, Zhang Y, Cotter D, Petri M. Medical Technology and Practice Patterns Institute, Bethesda, MD 20814

OBJECTIVE: To estimate the effect of corticosteroids (prednisone dose) on permanent organ damage among persons with systemic lupus erythematosus (SLE). METHODS: We identified 525 patients with incident SLE in the Hopkins Lupus Cohort. At each visit, clinical activity indices, laboratory data, and treatment were recorded. The study population was followed from the month after the first visit until June 29, 2006, or attainment of irreversible organ damage, death, loss to follow-up, or receipt of pulse methylprednisolone therapy. We estimated the effect of cumulative average dose of prednisone on organ damage using a marginal structural model to adjust for time-dependent confounding by indication due to SLE disease activity.
RESULTS: Compared with non-prednisone use, the hazard ratio of organ damage for prednisone was 1.16 (95% CI 0.54, 2.50) for cumulative average doses > 0-180 mg/month, 1.50 (95% CI 0.58, 3.88) for > 180-360 mg/month, 1.64 (95% CI 0.58, 4.69) for > 360-540 mg/month, and 2.51 (95% CI 0.87, 7.27) for > 540 mg/month. In contrast, standard Cox regression models estimated higher hazard ratios at all dose levels.
CONCLUSION: Our results suggest that low doses of prednisone do not result in a substantially increased risk of irreversible organ damage.
PMID: 19208608 [PubMed - indexed for MEDLINE]

December 2011

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CER Scan [Epub ahead of print]

1. Drug Saf. 2011 Jan 2012; 35(1):61-78 [Epub ahead of print]

Identifying Adverse Events of Vaccines Using a Bayesian Method of Medically Guided Information Sharing. Crooks CJ, Prieto-Merino D, Evans SJ. Division of Epidemiology and Public Health, University of Nottingham, Nottingham, UK.

Background: The detection of adverse events following immunization (AEFI) fundamentally depends on how these events are classified. Standard methods impose a choice between either grouping similar events together to gain power or splitting them into more specific definitions. We demonstrate a method of medically guided Bayesian information sharing that avoids grouping or splitting the data, and we further combine this with the standard epidemiological tools of stratification and multivariate regression. Objective: The aim of this study was to assess the ability of a Bayesian hierarchical model to identify gastrointestinal AEFI in children, and then combine this with testing for effect modification and adjustments for confounding. Study Design: Reporting odds ratios were calculated for each gastrointestinal AEFI and vaccine combination. After testing for effect modification, these were then re-estimated using multivariable logistic regression adjusting for age, sex, year and country of report. A medically guided hierarchy of AEFI terms was then derived to allow information sharing in a Bayesian model. Setting: All spontaneous reports of AEFI in children under 18 years of age in the WHO VigiBase™ (Uppsala Monitoring Centre, Uppsala, Sweden) before June 2010. Reports with missing age were included in the main analysis in a separate category and excluded in a subsequent sensitivity analysis. Exposures: The 15 most commonly prescribed childhood vaccinations, excluding influenza vaccines. Main Outcome Measures: All gastrointestinal AEFI coded by WHO Adverse Reaction Terminology. Results: A crude analysis identified 132 signals from 655 reported combinations of gastrointestinal AEFI. Adjusting for confounding by age, sex, year of report and country of report, where appropriate, reduced the number of signals identified to 88. The addition of a Bayesian hierarchical model identified four further signals and removed three. Effect modification by age and sex was identified for six vaccines for the outcomes of vomiting, nausea, diarrhoea and salivary gland enlargement.
Conclusion: This study demonstrated a sequence of methods for routinely analysing spontaneous report databases that was easily understandable and reproducible. The combination of classical and Bayesian methods in this study help to focus the limited resources for hypothesis testing studies towards adverse events with the strongest support from the data.
PMID: 22136183 [PubMed - as supplied by publisher]

CER Scan [published within the last 30 days]

1. Am J Epidemiol. 2011 Dec 1;174(11):1213-22. Epub 2011 Oct 24.

Effects of adjusting for instrumental variables on bias and precision of effect estimates.
Myers JA, Rassen JA, Gagne JJ, Huybrechts KF, Schneeweiss S, Rothman KJ, Joffe MM, Glynn RJ.

Recent theoretical studies have shown that conditioning on an instrumental variable (IV), a variable that is associated with exposure but not associated with outcome except through exposure, can increase both bias and variance of exposure effect estimates. Although these findings have obvious implications in cases of known IVs, their meaning remains unclear in the more common scenario where investigators are uncertain whether a measured covariate meets the criteria for an IV or rather a confounder. The authors present results from two simulation studies designed to provide insight into the problem of conditioning on potential IVs in routine epidemiologic practice. The simulations explored the effects of conditioning on IVs, near-IVs (predictors of exposure that are weakly associated with outcome), and confounders on the bias and variance of a binary exposure effect estimate. The results indicate that effect estimates which are conditional on a perfect IV or near-IV may have larger bias and variance than the unconditional estimate. However, in most scenarios considered, the increases in error due to conditioning were small compared with the total estimation error. In these cases, minimizing unmeasured confounding should be the priority when selecting variables for adjustment, even at the risk of conditioning on IVs.
PMID: 22025356 [PubMed - in process]

2. Am J Epidemiol. 2011 Dec 1;174(11):1223-7. Epub 2011 Oct 27.

Invited commentary: understanding bias amplification. Pearl J.

In choosing covariates for adjustment or inclusion in propensity score analysis, researchers must weigh the benefit of reducing confounding bias carried by those covariates against the risk of amplifying residual bias carried by unmeasured confounders. The latter is characteristic of covariates that act like instrumental variables-that is, variables that are more strongly associated with the exposure than with the outcome. In this issue of the Journal (Am J Epidemiol. 2011;174(11):1213-1222), Myers et al. compare the bias amplification of a near-instrumental variable with its bias-reducing potential and suggest that, in practice, the latter outweighs the former. The author of this commentary sheds broader light on this comparison by considering the cumulative effects of conditioning on multiple covariates and showing that bias amplification may build up at a faster rate than bias reduction. The author further derives a partial order on sets of covariates which reveals preference for conditioning on outcome-related, rather than exposure-related, confounders.
PMCID: PMC3224255 [Available on 2012/12/1] PMID: 22034488 [PubMed - in process]

3. Am J Epidemiol. 2011 Dec 1;174(11):1228-9. Epub 2011 Oct 24. Myers et Al. Response to “understanding bias amplification”. Myers JA, Rassen JA, Gagne JJ, Huybrechts KF, Schneeweiss S, Rothman KJ, Glynn RJ.

 

Response to Invited Commentary
PMID: 22025355 [PubMed - in process]

4. Epidemiology. 2011 Nov;22(6):815-22.

Estimating bias from loss to follow-up in the Danish National Birth Cohort. Greene N, Greenland S, Olsen J, Nohr EA. Department of Epidemiology, School of Public Health, University of California

Loss to follow-up in cohort studies may result in biased association estimates. Of 61,895 women entering the Danish National Birth Cohort and completing the first data-collection phase, 37,178 (60%) opted to be in the 7-year follow-up. Using national registry data to obtain end point information on all members of the cohort, we estimated associations in the baseline and the 7-year follow-up participant populations for 5 exposure-outcome associations: (a) size at birth and childhood asthma, (b) assisted reproductive treatment and childhood hospitalizations, (c) prepregnancy body mass index and childhood infections, (d) alcohol drinking in early pregnancy and childhood developmental disorders, and (e) maternal smoking in pregnancy and childhood attention-deficit hyperactivity disorder (ADHD). We estimated follow-up bias in the odds or rate ratios by calculating relative ratios. For all but one of the above analyses, the bias appeared to be small, between -10% and +8%. For maternal smoking in pregnancy and childhood ADHD, we estimated a positive bias of approximately 33% (95% bootstrap limits of -30% and +152%). The presence and magnitude of bias due to loss to follow-up depended on the nature of the factors or outcomes examined, with the most pronounced contribution in this study coming from maternal smoking. Our methods and results may inform bias analyses in future pregnancy cohort studies.
PMID: 21918455 [PubMed - in process]

DECEMBER THEME: Methods for Addressing Missing Data in CER

1. Stat Med. 2011 Dec 4. doi: 10.1002/sim.4413. [Epub ahead of print]

Diagnosing imputation models by applying target analyses to posterior replicates of completed data. He Y, Zaslavsky AM. Department of Health Care Policy, Harvard Medical School, Boston, MA, 02115, USA. he@hcp.med.harvard.edu.

Multiple imputation fills in missing data with posterior predictive draws from imputation models. To assess the adequacy of imputation models, we can compare completed data with their replicates simulated under the imputation model. We apply analyses of substantive interest to both datasets and use posterior predictive checks of the differences of these estimates to quantify the evidence of model inadequacy. We can further integrate out the imputed missing data and their replicates over the completed-data analyses to reduce variance in the comparison. In many cases, the checking procedure can be easily implemented using standard imputation software by treating re-imputations under the model as posterior predictive replicates. Thus, it can be applied for non-Bayesian imputation methods. We also sketch several strategies for applying the method in the context of practical imputation analyses. We illustrate the method using two real data applications and study its property using a simulation. Copyright © 2011 John Wiley & Sons, Ltd. Copyright © 2011 John Wiley & Sons, Ltd.
PMID: 22139814 [PubMed - as supplied by publisher]

2. Stat Methods Med Res. 2011 Mar 23. [Epub ahead of print]

Using causal diagrams to guide analysis in missing data problems. Daniel RM, Kenward MG, Cousens SN, De Stavola BL. Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK.

Estimating causal effects from incomplete data requires additional and inherently untestable assumptions regarding the mechanism giving rise to the missing data. We show that using causal diagrams to represent these additional assumptions both complements and clarifies some of the central issues in missing data theory, such as Rubin’s classification of missingness mechanisms (as missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR)) and the circumstances in which causal effects can be estimated without bias by analysing only the subjects with complete data. In doing so, we formally extend the back-door criterion of Pearl and others for use in incomplete data examples. These ideas are illustrated with an example drawn from an occupational cohort study of the effect of cosmic radiation on skin cancer incidence.
PMID: 21389091 [PubMed - as supplied by publisher]

3. Stat Med. 2011 Mar 15;30(6):627-41. doi: 10.1002/sim.4124. Epub 2010 Dec 28.

Estimating propensity scores with missing covariate data using general location mixture models. Mitra R, Reiter JP. School of Mathematics, University of Southampton, Southampton, SO17 1BJ, U.K. R.Mitra@soton.ac.uk

In many observational studies, analysts estimate causal effects using propensity scores, e.g. by matching, sub-classifying, or inverse probability weighting based on the scores. Estimation of propensity scores is complicated when some values of the covariates are missing. Analysts can use multiple imputation to create completed data sets from which propensity scores can be estimated. We propose a general location mixture model for imputations that assumes that the control units are a latent mixture of (i) units whose covariates are drawn from the same distributions as the treated units’ covariates and (ii) units whose covariates are drawn from different distributions. This formulation reduces the influence of control units outside the treated units’ region of the covariate space on the estimation of parameters in the imputation model, which can result in more plausible imputations. In turn, this can result in more reliable estimates of propensity scores and better balance in the true covariate distributions when matching or sub-classifying. We illustrate the benefits of the latent class modeling approach with simulations and with an observational study of the effect of breast feeding on children’s cognitive abilities. Copyright © 2010 John Wiley & Sons, Ltd.
PMID: 21337358 [PubMed - indexed for MEDLINE]

4. Am J Epidemiol. 2010 Nov 1;172(9):1070-6. Epub 2010 Sep 14.

Multiple imputation for missing data via sequential regression trees. Burgette LF, Reiter JP. Department of Statistical Science, Duke University, Durham, North Carolina 27708.

Multiple imputation is particularly well suited to deal with missing data in large epidemiologic studies, because typically these studies support a wide range of analyses by many data users. Some of these analyses may involve complex modeling, including interactions and nonlinear relations. Identifying such relations and encoding them in imputation models, for example, in the conditional regressions for multiple imputation via chained equations, can be daunting tasks with large numbers of categorical and continuous variables. The authors present a nonparametric approach for implementing multiple imputation via chained equations by using sequential regression trees as the conditional models. This has the potential to capture complex relations with minimal tuning by the data imputer. Using simulations, the authors demonstrate that the method can result in more plausible imputations, and hence more reliable inferences, in complex settings than the naive application of standard sequential regression imputation techniques. They apply the approach to impute missing values in data on adverse birth outcomes with more than 100 clinical and survey variables. They evaluate the imputations using posterior predictive checks with several epidemiologic analyses of interest.
PMID: 20841346 [PubMed - indexed for MEDLINE]

Free Full Text: http://aje.oxfordjournals.org/content/172/9/1070.long

5. Artif Intell Med. 2010 Oct;50(2):105-15. Epub 2010 Jul 16.

Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Jerez JM, Molina I, García-Laencina PJ, Alba E, Ribelles N, Martín M, Franco L. Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, E.T.S.I. Informática, Campus de Teatinos s/n, 29071 Málaga, Spain. jja@lcc.uma.es

OBJECTIVES: Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. This work evaluates the performance of several statistical and machine learning imputation methods that were used to predict recurrence in patients in an extensive real breast cancer data set.
MATERIALS AND METHODS: Imputation methods based on statistical techniques, e.g., mean, hot-deck and multiple imputation, and machine learning techniques, e.g., multi-layer perceptron (MLP), self-organisation maps (SOM) and k-nearest neighbour (KNN), were applied to data collected through the “El Álamo-I” project, and the results were then compared to those obtained from the listwise deletion
(LD) imputation method. The database includes demographic, therapeutic and recurrence-survival information from 3679 women with operable invasive breast cancer diagnosed in 32 different hospitals belonging to the Spanish Breast Cancer Research Group (GEICAM). The accuracies of predictions on early cancer relapse were measured using artificial neural networks (ANNs), in which different ANNs were estimated using the data sets with imputed missing values.
RESULTS: The imputation methods based on machine learning algorithms outperformed imputation statistical methods in the prediction of patient outcome. Friedman’s test revealed a significant difference (p=0.0091) in the observed area under the ROC curve (AUC) values, and the pairwise comparison test showed that the AUCs for MLP, KNN and SOM were significantly higher (p=0.0053, p=0.0048 and p=0.0071, respectively) than the AUC from the LD-based prognosis model.
CONCLUSION: The methods based on machine learning techniques were the most suited for the imputation of missing values and led to a significant enhancement of prognosis accuracy compared to imputation methods based on statistical procedures.
Copyright © 2010 Elsevier B.V. All rights reserved.
PMID: 20638252 [PubMed - indexed for MEDLINE]

6. J Clin Epidemiol. 2010 Jul;63(7):728-36. Epub 2010 Mar 25.

Unpredictable bias when using the missing indicator method or complete case analysis for missing confounder values: an empirical example. Knol MJ, Janssen KJ, Donders AR, Egberts AC, Heerdink ER, Grobbee DE, Moons KG, Geerlings MI. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Str. 6.131, PO Box 85500, 3508 GA Utrecht, The Netherlands. m.j.knol@umcutrecht.nl

OBJECTIVE: Missing indicator method (MIM) and complete case analysis (CC) are frequently used to handle missing confounder data. Using empirical data, we demonstrated the degree and direction of bias in the effect estimate when using these methods compared with multiple imputation (MI).
STUDY DESIGN AND SETTING: From a cohort study, we selected an exposure (marital status), outcome (depression), and confounders (age, sex, and income). Missing values in “income” were created according to different patterns of missingness: missing values were created completely at random and depending on exposure and outcome values. Percentages of missing values ranged from 2.5% to 30%.
RESULTS: When missing values were completely random, MIM gave an overestimation of the odds ratio, whereas CC and MI gave unbiased results. MIM and CC gave under- or overestimations when missing values depended on observed values. Magnitude and direction of bias depended on how the missing values were related to exposure and outcome. Bias increased with increasing percentage of missing
values.
CONCLUSION: MIM should not be used in handling missing confounder data because it gives unpredictable bias of the odds ratio even with small percentages of missing values. CC can be used when missing values are completely random, but it gives loss of statistical power.
Copyright 2010 Elsevier Inc. All rights reserved.
PMID: 20346625 [PubMed - indexed for MEDLINE]

7. Pharmacoepidemiol Drug Saf. 2010 Jun;19(6):618-26.

Issues in multiple imputation of missing data for large general practice clinical databases. Marston L, Carpenter JR, Walters KR, Morris RW, Nazareth I, Petersen I. Department of Primary Care and Population Health, University College London, Rowland Hill Street, London NW32PF

PURPOSE: Missing data are a substantial problem in clinical databases. This paper aims to examine patterns of missing data in a primary care database, compare this to nationally representative datasets and explore the use of multiple imputation (MI) for these data.
METHODS: The patterns and extent of missing health indicators in a UK primary care database (THIN) were quantified using 488 384 patients aged 16 or over in their first year after registration with a GP from 354 General Practices. MI models were developed and the resulting data compared to that from nationally representative datasets (14 142 participants aged 16 or over from the Health Survey for England 2006 (HSE) and 4 252 men from the British Regional Heart Study (BRHS)).
RESULTS: Between 22% (smoking) and 38% (height) of health indicator data were missing in newly registered patients, 2004-2006. Distributions of height, weight and blood pressure were comparable to HSE and BRHS, but alcohol and smoking were not. After MI the percentage of smokers and non-drinkers was higher in THIN than the comparison datasets, while the percentage of ex-smokers and heavy drinkers was lower. Height, weight and blood pressure remained similar to the comparison datasets.
CONCLUSIONS: Given available data, the results are consistent with smoking and alcohol data missing not at random whereas height, weight and blood pressure missing at random. Further research is required on suitable imputation methods for smoking and alcohol in such databases.
PMID: 20306452 [PubMed - indexed for MEDLINE]

8. Circ Cardiovasc Qual Outcomes. 2010 Jan;3(1):98-105.

Missing data analysis using multiple imputation: getting to the heart of the matter. He Y. Department of Health Care Policy, Harvard Medical School

Missing data are a pervasive problem in health investigations. We describe some background of missing data analysis and criticize ad hoc methods that are prone to serious problems. We then focus on multiple imputation, in which missing cases are first filled in by several sets of plausible values to create multiple completed datasets, then standard complete-data procedures are applied to each completed dataset, and finally the multiple sets of results are combined to yield a single inference. We introduce the basic concepts and general methodology and provide some guidance for application. For illustration, we use a study assessing the effect of cardiovascular diseases on hospice discussion for late stage lung cancer patients.
PMCID: PMC2818781; PMID: 20123676 [PubMed - indexed for MEDLINE]

Free PDF: http://circoutcomes.ahajournals.org/content/3/1/98.full.pdf+html

9. Am J Epidemiol. 2010 Mar 1;171(5):624-32. Epub 2010 Jan 27.

Multiple imputation for missing data: fully conditional specification versus multivariate normal imputation. Lee KJ, Carlin JB. Clinical Epidemiology and Biostatistics Unit, Murdoch Childrens Research Institute, Royal Children’s Hospital, Flemington Road, Parkville, Victoria

Statistical analysis in epidemiologic studies is often hindered by missing data, and multiple imputation is increasingly being used to handle this problem. In a simulation study, the authors compared 2 methods for imputation that are widely available in standard software: fully conditional specification (FCS) or “chained equations” and multivariate normal imputation (MVNI). The authors created data sets of 1,000 observations to simulate a cohort study, and missing data were induced under 3 missing-data mechanisms. Imputations were performed using FCS (Royston’s “ice”) and MVNI (Schafer’s NORM) in Stata (Stata Corporation, College Station, Texas), with transformations or prediction matching being used to manage nonnormality in the continuous variables. Inferences for a set of regression parameters were compared between these approaches and a complete-case analysis. As expected, both FCS and MVNI were generally less biased than complete-case analysis, and both produced similar results despite the presence of binary and ordinal variables that clearly did not follow a normal distribution. Ignoring
skewness in a continuous covariate led to large biases and poor coverage for the corresponding regression parameter under both approaches, although inferences for other parameters were largely unaffected. These results provide reassurance that similar results can be expected from FCS and MVNI in a standard regression analysis involving variously scaled variables.
PMID: 20106935 [PubMed - indexed for MEDLINE]

Free Full Text: http://aje.oxfordjournals.org/content/171/5/624.long

10. J Sch Psychol. 2010 Feb;48(1):5-37.

An introduction to modern missing data analyses. Baraldi AN, Enders CK. Arizona State University, USA. Amanda.Baraldi@asu.edu

A great deal of recent methodological research has focused on two modern missing data analysis methods: maximum likelihood and multiple imputation. These approaches are advantageous to traditional techniques (e.g. deletion and mean imputation techniques) because they require less stringent assumptions and mitigate the pitfalls of traditional techniques. This article explains the theoretical underpinnings of missing data analyses, gives an overview of traditional missing data techniques, and provides accessible descriptions of maximum likelihood and multiple imputation. In particular, this article focuses on maximum likelihood estimation and presents two analysis examples from the Longitudinal Study of American Youth data. One of these examples includes a description of the use of auxiliary variables. Finally, the paper illustrates ways that researchers can use intentional, or planned, missing data to enhance their research designs.
PMID: 20006986 [PubMed - indexed for MEDLINE]

11. Int J Epidemiol. 2010 Feb;39(1):118-28. Epub 2009 Oct 25.

Modelling relative survival in the presence of incomplete data: a tutorial. Nur U, Shack LG, Rachet B, Carpenter JR, Coleman MP. Cancer Research UK Cancer Survival Group, London School of Hygiene and Tropical Medicine, London, UK. ula.nur@lshtm.ac.uk

BACKGROUND: Missing data frequently create problems in the analysis of population-based data sets, such as those collected by cancer registries. Restriction of analysis to records with complete data may yield inferences that are substantially different from those that would have been obtained had no data been missing. ‘Naive’ methods for handling missing data, such as restriction of the analysis to complete records or creation of a ‘missing’ category, have drawbacks that can invalidate the conclusions from the analysis. We offer a tutorial on modern methods for handling missing data in relative survival analysis.
METHODS: We estimated relative survival for 29 563 colorectal cancer patients who were diagnosed between 1997 and 2004 and registered in the North West Cancer Intelligence Service. The method of multiple imputation (MI) was applied to account for the common example of incomplete stage at diagnosis, under the missing at random (MAR) assumption. Multivariable regression with a generalized linear model and Poisson error structure was then used to estimate the excess hazard of death of the colorectal cancer patients, over and above the background mortality, adjusting for significant predictors of mortality.
RESULTS: Incomplete information on stage, morphology and grade meant that only 55% of the data could be included in the ‘complete-case’ analysis. All cases could be included after indicator method (IM) or MI method. Handling missing data by MI produced a significantly lower estimate of the excess mortality for stage, morphology and grade, with the largest reductions occurring for late-stage and high-grade tumours, when compared with the results of complete-case analysis.
CONCLUSION: In complete-case analysis, almost 50% of the information could not be included, and with the IM, all records with missing values for stage were combined into a single ‘missing’ category. We show that MI methods greatly improved the results by exploiting all the information in the incomplete records. This method also helped to ensure efficient inferences about survival were made from the multivariate regression analyses.
PMID: 19858106 [PubMed - indexed for MEDLINE]

Free Full Text: http://ije.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=19858106

November 2011

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CER Scan [Epub ahead of print]

1. Clin Pharmacol Ther. 2011 Nov 2. doi: 10.1038/clpt.2011.235. [Epub ahead of print]

Assessing the Comparative Effectiveness of Newly Marketed Medications: Methodological Challenges and Implications for Drug Development. Schneeweiss S, Gagne JJ, Glynn RJ, Ruhl M, Rassen JA. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA

Comparative-effectiveness research (CER) aims to produce actionable evidence regarding the effectiveness and safety of medical products and interventions as they are used outside of controlled research settings. Although CER evidence regarding medications is particularly needed shortly after market approval, key methodological challenges include (i) potential bias due to channeling of patients to the newly marketed medication because of various patient-, physician-, and system-related factors; (ii) rapid changes in the characteristics of the user population during the early phase of marketing; and (iii) lack of timely data and the often small number of users in the first few months of marketing. We propose a mix of approaches to generate comparative-effectiveness data in the early marketing period, including sequential cohort monitoring with secondary health-care data and propensity score (PS) balancing, as well as extended follow-up of phase III and phase IV trials, indirect comparisons of placebo-controlled trials, and modeling and simulation of virtual trials.
PMID: 22048230 [PubMed - as supplied by publisher]

2. Ann Epidemiol. 2011 Oct 28. [Epub ahead of print]

Antidepressant Use and Cognitive Deficits in Older Men: Addressing Confounding by Indications with Different Methods. Han L, Kim N, Brandt C, Allore HG. Yale University Internal Medicine Program on Aging, New Haven, CT.

PURPOSE: Antidepressant use has been associated with cognitive impairment in older persons. We sought to examine whether this association might reflect an indication bias.

METHODS: A total of 544 community-dwelling hypertensive men aged =65 years completed the Hopkins Verbal Learning Test at baseline and 1 year. Antidepressant medications were ascertained by the use of medical records. Potential confounding by indications was examined by adjusting for depression-related diagnoses and severity of depression symptoms using multiple linear regression, a propensity score, and a structural equation model (SEM).

RESULTS: Before adjusting for the indications, a one unit cumulative exposure to antidepressants was associated with -1.00 (95% confidence interval [CI], -1.94, -0.06) point lower HVLT score. After adjusting for the indications using multiple linear regression or a propensity score, the association diminished to -0.48 (95% CI, -0.62, 1.58) and -0.58 (95% CI, -0.60, 1.58), respectively. The most clinical interpretable empirical SEM with adequate fit involves both direct and indirect paths of the two indications. Depression-related diagnoses and depression symptoms significantly predict antidepressant use (p < .05). Their total standardized path coefficients on Hopkins Verbal Learning Test score were twice (0.073) or as large (0.034) as the antidepressant use (0.035).

CONCLUSION: The apparent association between antidepressant use and memory deficit in older persons may be confounded by indications. SEM offers a heuristic empirical method for examining confounding by indications but not quantitatively superior bias reduction compared with conventional methods.
PMID: 22037381 [PubMed - as supplied by publisher]

3. Stat Methods Med Res. 2011 Oct 19. [Epub ahead of print]

Observational data for comparative effectiveness research: An emulation of randomised trials of statins and primary prevention of coronary heart disease. Danaei G, García Rodríguez LA, Cantero OF, Logan R, Hernán MA. Department of Epidemiology, Harvard School of Public Health, Boston, MA.

This article reviews methods for comparative effectiveness research using observational data. The basic idea is using an observational study to emulate a hypothetical randomised trial by comparing initiators versus non-initiators of treatment. After adjustment for measured baseline confounders, one can then conduct the observational analogue of an intention-to-treat analysis. We also explain two approaches to conduct the analogues of per-protocol and as-treated analyses after further adjusting for measured time-varying confounding and selection bias using inverse-probability weighting. As an example, we implemented these methods to estimate the effect of statins for primary prevention of coronary heart disease (CHD) using data from electronic medical records in the UK. Despite strong confounding by indication, our approach detected a potential benefit of statin therapy. The analogue of the intention-to-treat hazard ratio (HR) of CHD was 0.89 (0.73, 1.09) for statin initiators versus non-initiators. The HR of CHD was 0.84 (0.54, 1.30) in the per-protocol analysis and 0.79 (0.41, 1.41) in the as-treated analysis for 2 years of use versus no use. In contrast, a conventional comparison of current users versus never users of statin therapy resulted in a HR of 1.31 (1.04, 1.66). We provide a flexible and annotated SAS program to implement the proposed analyses.
PMID: 22016461 [PubMed - as supplied by publisher]

4. Clin Trials. 2011 Oct 12. [Epub ahead of print]

Challenges in the design and implementation of the Multicenter Uveitis Steroid Treatment (MUST) Trial – lessons for comparative effectiveness trials. Holbrook JT, Kempen JH, Prusakowski NA, Altaweel MM, Jabs DA. Center for Clinical Trials, Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.

BACKGROUND: Randomized clinical trials (RCTs) are an important component of comparative effectiveness (CE) research because they are the optimal design for head-to-head comparisons of different treatment options.

PURPOSE: To describe decisions made in the design of the Multicenter Uveitis Steroid Treatment (MUST) Trial to ensure that the results would be widely generalizable.

METHODS: Review of design and implementation decisions and their rationale for the trial.

RESULTS: The MUST Trial is a multicenter randomized controlled CE trial evaluating a novel local therapy (intraocular fluocinolone acetonide implant) versus the systemic therapy standard of care for noninfectious uveitis. Decisions made in protocol design in order to broaden enrollment included allowing patients with very poor vision and media opacity to enroll and including clinical sites outside the United States. The treatment protocol was designed to follow standard care. The primary outcome, visual acuity, is important to patients and can be evaluated in all eyes with uveitis. Other outcomes include patient-reported visual function, quality of life, and disease and treatment related complications.

LIMITATIONS: The trial population is too small for subgroup analyses that are of interest and the trial is being conducted at tertiary medical centers.

CONCLUSION: CE trials require greater emphasis on generalizability than many RCTs but otherwise face similar challenges for design choices as any RCT. The increase in heterogeneity in patients and treatment required to ensure generalizability can be balanced with a rigorous approach to implementation, outcome assessment, and statistical design. This approach requires significant resources that may limit implementation in many RCTs, especially in clinical practice settings. Clinical Trials 2011; XX: 1-8. http://ctj.sagepub.com.
PMID: 21994128 [PubMed - as supplied by publisher]

5. Stat Methods Med Res. 2011 Oct 3. [Epub ahead of print]

Assessing the sensitivity of methods for estimating principal causal effects. Stuart EA, Jo B. Departments of Mental Health and Biostatistics, Johns Hopkins Bloomberg School of Public Health, 624 N Broadway, 8th Floor, Baltimore, MD, USA.

The framework of principal stratification provides a way to think about treatment effects conditional on post-randomization variables, such as level of compliance. In particular, the complier average causal effect (CACE) – the effect of the treatment for those individuals who would comply with their treatment assignment under either treatment condition – is often of substantive interest. However, estimation of the CACE is not always straightforward, with a variety of estimation procedures and underlying assumptions, but little advice to help
researchers select between methods. In this article, we discuss and examine two methods that rely on very different assumptions to estimate the CACE: a maximum likelihood (‘joint’) method that assumes the ‘exclusion restriction,’ (ER) and a propensity score-based method that relies on ‘principal ignorability.’ We detail the assumptions underlying each approach, and assess each methods’ sensitivity to both its own assumptions and those of the other method using both simulated data and a motivating example. We find that the ER-based joint approach appears somewhat less sensitive to its assumptions, and that the performance of both methods is significantly improved when there are strong predictors of compliance. Interestingly, we also find that each method performs particularly well when the assumptions of the other approach are violated. These results highlight the importance of carefully selecting an estimation procedure whose assumptions are likely to be satisfied in practice and of having strong predictors of principal stratum membership.
PMID: 21971481 [PubMed - as supplied by publisher]

CER Scan [published within the last 30 days]

1. Am J Epidemiol. 2011 Nov 15;174(10):1204-10. Epub 2011 Oct 7.

Comparing different strategies for timing of dialysis initiation through inverse probability weighting. Sjölander A, Nyrén O, Bellocco R, Evans M.

Dialysis has been used in the treatment of patients with end-stage renal disease since the 1960s. Recently, several large observational studies have been conducted to assess whether early initiation of dialysis prolongs survival, as compared with late initiation. However, these studies have used analytic approaches which are likely to suffer from either lead-time bias or immortal-time bias. In this paper, the authors demonstrate that recently developed methods in the causal inference literature can be used to avoid both types of bias and accurately estimate the ideal time for dialysis initiation from observational data. This is illustrated using data from a nationwide population-based cohort of patients with chronic kidney disease in Sweden (1996-2003).
PMID: 21984655 [PubMed - in process]

2. BMJ. 2011 Oct 3;343:d5888. doi: 10.1136/bmj.d5888.

Estimating treatment effects for individual patients based on the results of randomised clinical trials. Dorresteijn JA, Visseren FL, Ridker PM, Wassink AM, Paynter NP, Steyerberg EW, van der Graaf Y, Cook NR. Department of Vascular Medicine, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, Netherlands.

OBJECTIVES: To predict treatment effects for individual patients based on data from randomised trials, taking rosuvastatin treatment in the primary prevention of cardiovascular disease as an example, and to evaluate the net benefit of making treatment decisions for individual patients based on a predicted absolute treatment effect.

SETTING: As an example, data were used from the Justification for the Use of Statins in Prevention (JUPITER) trial, a randomised controlled trial evaluating the effect of rosuvastatin 20 mg daily versus placebo on the occurrence of
cardiovascular events (myocardial infarction, stroke, arterial revascularisation, admission to hospital for unstable angina, or death from cardiovascular causes). Population 17,802 healthy men and women who had low density lipoprotein cholesterol levels of less than 3.4 mmol/L and high sensitivity C reactive protein levels of 2.0 mg/L or more.

METHODS: Data from the Justification for the Use of Statins in Prevention trial were used to predict rosuvastatin treatment effect for individual patients based on existing risk scores (Framingham and Reynolds) and on a newly developed prediction model. We compared the net benefit of prediction based rosuvastatin treatment (selective treatment of patients whose predicted treatment effect exceeds a decision threshold) with the net benefit of treating either everyone or no one.

RESULTS: The median predicted 10 year absolute risk reduction for cardiovascular events was 4.4% (interquartile range 2.6-7.0%) based on the Framingham risk score, 4.2% (2.5-7.1%) based on the Reynolds score, and 3.9% (2.5-6.1%) based on the newly developed model (optimal fit model). Prediction based treatment was associated with more net benefit than treating everyone or no one, provided that the decision threshold was between 2% and 7%, and thus that the number willing to treat (NWT) to prevent one cardiovascular event over 10 years was between 15 and 50.

CONCLUSIONS: Data from randomised trials can be used to predict treatment effect in terms of absolute risk reduction for individual patients, based on a newly developed model or, if available, existing risk scores. The value of such prediction of treatment effect for medical decision making is conditional on the NWT to prevent one outcome event. Trial registration number Clinicaltrials.gov NCT00239681.
PMCID: PMC3184644
PMID: 21968126 [PubMed - in process]

Free Full Text: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3184644/?tool=pubmed

3. BMC Med Res Methodol. 2011 Sep 21;11:132.

Benefits of ICU admission in critically ill patients: whether instrumental variable methods or propensity scores should be used. Pirracchio R, Sprung C, Payen D, Chevret S. Département de Biostatistique et Informatique Médicale, Unité INSERM UMR 717, Hôpital Saint Louis, APHP, Paris, 75010, France. romainpirracchio@yahoo.fr

BACKGROUND: The assessment of the causal effect of Intensive Care Unit (ICU) admission generally involves usual observational designs and thus requires controlling for confounding variables. Instrumental variable analysis is an econometric technique that allows causal inferences of the effectiveness of some treatments during situations to be made when a randomized trial has not been or cannot be conducted. This technique relies on the existence of one variable or “instrument” that is supposed to achieve similar observations with a different treatment for “arbitrary” reasons, thus inducing substantial variation in the treatment decision with no direct effect on the outcome. The objective of the study was to assess the benefit in terms of hospital mortality of ICU admission in a cohort of patients proposed for ICU admission (ELDICUS cohort).

METHODS: Using this cohort of 8,201 patients triaged for ICU (including 6,752 (82.3%) patients admitted), the benefit of ICU admission was evaluated using 3 different approaches: instrumental variables, standard regression and propensity score matched analyses. We further evaluated the results obtained using different instrumental variable methods that have been proposed for dichotomous outcomes.

RESULTS: The physician’s main specialization was found to be the best instrument. All instrumental variable models adequately reduced baseline imbalances, but failed to show a significant effect of ICU admission on hospital mortality, with confidence intervals far higher than those obtained in standard or propensity-based analyses.

CONCLUSIONS: Instrumental variable methods offer an appealing alternative to handle the selection bias related to nonrandomized designs, especially when the presence of significant unmeasured confounding is suspected. Applied to the ELDICUS database, this analysis failed to show any significant beneficial effect of ICU admission on hospital mortality. This result could be due to the lack of statistical power of these methods.
PMCID: PMC3185268
PMID: 21936926 [PubMed - in process]

Free Full Text: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3185268/?tool=pubmed

4. Med Care. 2011 Oct;49(10):940-7.

The mortality risk score and the ADG score: two points-based scoring systems for the johns hopkins aggregated diagnosis groups to predict mortality in a general adult population cohort in Ontario, Canada. Austin PC, Walraven C. Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. peter.austin@ices.on.ca

BACKGROUND: Logistic regression models that incorporated age, sex, and indicator variables for the Johns Hopkins’ Aggregated Diagnosis Groups (ADGs) categories have been shown to accurately predict all-cause mortality in adults.

OBJECTIVES: To develop 2 different point-scoring systems using the ADGs. The Mortality Risk Score (MRS) collapses age, sex, and the ADGs to a single summary score that predicts the annual risk of all-cause death in adults. The ADG Score derives weights for the individual ADG diagnosis groups.

RESEARCH DESIGN: Retrospective cohort constructed using population-based administrative data.

PARTICIPANTS: All 10,498,413 residents of Ontario, Canada, between the age of 20 and 100 years who were alive on their birthday in 2007, participated in this study. Participants were randomly divided into derivation and validation samples.

MEASURES: Death within 1 year.

RESULTS: In the derivation cohort, the MRS ranged from -21 to 139 (median value 29, IQR 17 to 44). In the validation group, a logistic regression model with the MRS as the sole predictor significantly predicted the risk of 1-year mortality with a c-statistic of 0.917. A regression model with age, sex, and the ADG Score has similar performance. Both methods accurately predicted the risk of 1-year mortality across the 20 vigintiles of risk.

CONCLUSIONS: The MRS combined values for a person’s age, sex, and the John Hopkins ADGs to accurately predict 1-year mortality in adults. The ADG Score is a weighted score representing the presence or absence of the 32 ADG diagnosis groups. These scores will facilitate health services researchers conducting risk adjustment using administrative health care databases.
PMID: 21921849 [PubMed - in process]

5. Stat Med. 2011 Oct 30;30(24):2947-58. doi: 10.1002/sim.4324. Epub 2011 Jul 29.

Analyzing direct and indirect effects of treatment using dynamic path analysis applied to data from the Swiss HIV Cohort Study. Røysland K, Gran JM, Ledergerber B, von Wyl V, Young J, Aalen OO. Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Norway. kjetil.roysland@medisin.uio.no

When applying survival analysis, such as Cox regression, to data from major clinical trials or other studies, often only baseline covariates are used. This is typically the case even if updated covariates are available throughout the observation period, which leaves large amounts of information unused. The main reason for this is that such time-dependent covariates often are internal to the disease process, as they are influenced by treatment, and therefore lead to confounded estimates of the treatment effect. There are, however, methods to exploit such covariate information in a useful way. We study the method of dynamic path analysis applied to data from the Swiss HIV Cohort Study. To adjust for time-dependent confounding between treatment and the outcome ‘AIDS or death’, we carried out the analysis on a sequence of mimicked randomized trials constructed from the original cohort data. To analyze these trials together, regular dynamic path analysis is extended to a composite analysis of weighted dynamic path models. Results using a simple path model, with one indirect effect mediated through current HIV-1 RNA level, show that most or all of the total effect go through HIV-1 RNA for the first 4?years. A similar model, but with CD4 level as mediating variable, shows a weaker indirect effect, but the results are in the same direction. There are many reasons to be cautious when drawing conclusions from estimates of direct and indirect effects. Dynamic path analysis is however a useful tool to explore underlying processes, which are ignored in regular analyses.
PMID: 21800346 [PubMed - in process]

6. Epidemiology. 2011 Sep;22(5):718-23.

A comparison of methods to estimate the hazard ratio under conditions of time-varying confounding and nonpositivity. Naimi AI, Cole SR, Westreich DJ, Richardson DB. Department of Epidemiology, Gillings School of Global Public Health, UNC-Chapel Hill, NC 27599, USA.

In occupational epidemiologic studies, the healthy worker survivor effect refers to a process that leads to bias in the estimates of an association between cumulative exposure and a health outcome. In these settings, work status acts both as an intermediate and confounding variable and may violate the positivity assumption (the presence of exposed and unexposed observations in all strata of the confounder). Using Monte Carlo simulation, we assessed the degree to which crude, work-status adjusted, and weighted (marginal structural) Cox proportional hazards models are biased in the presence of time-varying confounding and nonpositivity. We simulated the data representing time-varying occupational exposure, work status, and mortality. Bias, coverage, and root mean squared error (MSE) were calculated relative to the true marginal exposure effect in a range of scenarios. For a base-case scenario, using crude, adjusted, and weighted Cox models, respectively, the hazard ratio was biased downward 19%, 9%, and 6%; 95% confidence interval coverage was 48%, 85%, and 91%; and root MSE was 0.20, 0.13, and 0.11. Although marginal structural models were less biased in most scenarios studied, neither standard nor marginal structural Cox proportional hazards models fully resolve the bias encountered under conditions of time-varying confounding and nonpositivity.
PMCID: PMC3155387 [Available on 2012/9/1]
PMID: 21747286 [PubMed - in process]

CER Scan [published within the last 90 days]

1. Stat Biosci. 2011 Sep;3(1):6-27.

Estimating Decision-Relevant Comparative Effects Using Instrumental Variables. Basu A. Departments of Health Services and Pharmacy, University of Washington, Seattle, 1959 NE Pacific St, Box 357660, Seattle, WA 98195-7660, USA.

Instrumental variables methods (IV) are widely used in the health economics literature to adjust for hidden selection biases in observational studies when estimating treatment effects. Less attention has been paid in the applied literature to the proper use of IVs if treatment effects are heterogeneous across subjects. Such a heterogeneity in effects becomes an issue for IV estimators when individuals’ self-selected choices of treatments are correlated with expected idiosyncratic gains or losses from treatments. We present an overview of the challenges that arise with IV estimators in the presence of effect heterogeneity and self-selection and compare conventional IV analysis with alternative approaches that use IVs to directly address these challenges. Using a Medicare sample of clinically localized breast cancer patients, we study the impact of breast-conserving surgery and radiation with mastectomy on 3-year survival rates. Our results reveal the traditional IV results may have masked important heterogeneity in treatment effects. In the context of these results, we discuss the advantages and limitations of conventional and alternative IV methods in estimating mean treatment-effect parameters, the role of heterogeneity in comparative effectiveness research and the implications for diffusion of technology.
PMCID: PMC3193796 [Available on 2012/9/1]
PMID: 22010051 [PubMed]

October 2011

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CER Scan [Epub ahead of print]

1. Pharmacoepidemiol Drug Saf. 2011 Sep 23. doi: 10.1002/pds.2251. [Epub ahead of print]

Balance measures for propensity score methods: a clinical example on beta-agonist use and the risk of myocardial infarction. Groenwold RH, de Vries F, de Boer A, Pestman WR, Rutten FH, Hoes AW, Klungel OH. Department of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands. r.h.h.groenwold@umcutrecht.nl.

PURPOSE: Propensity score (PS) methods aim to control for confounding by balancing confounders between exposed and unexposed subjects with the same PS. PS balance measures have been compared in simulated data but limited in empirical data. Our objective was to compare balance measures in clinical data and assessed the association between long-acting inhalation beta-agonist (LABA) use and myocardial infarction.

METHODS: We estimated the relationship between LABA use and myocardial infarction in a cohort of adults with a diagnosis of asthma or chronic obstructive pulmonary disorder from the Utrecht General Practitioner Research Network database. More than two thousand PS models, including information on the observed confounders age, sex, diabetes, cardiovascular disease and chronic obstructive pulmonary disorder status, were applied. The balance of these confounders was assessed using the standardised difference (SD), Kolmogorov-Smirnov (KS) distance and overlapping coefficient. Correlations between these balance measures were calculated. In addition, simulation studies were performed to assess the correlation between balance measures and bias.

RESULTS: LABA use was not related to myocardial infarction after conditioning on the PS (median heart rate=1.14, 95%CI=0.47-2.75). When using the different balance measures for selecting a PS model, similar associations were obtained. In our empirical data, SD and KS distance were highly correlated balance measures (r=0.92). In simulations, SD, KS distance and overlapping coefficient were similarly correlated to bias (e.g. r=0.55, r=0.52 and r=-0.57, respectively, when conditioning on the PS).

CONCLUSIONS: We recommend using the SD or the KS distance to quantify the balance of confounder distributions when applying PS methods. Copyright © 2011 John Wiley & Sons, Ltd.

PMID: 21953948 [PubMed - as supplied by publisher]

2. Clin Trials. 2011 Sep 23. [Epub ahead of print]

Beyond the intention-to-treat in comparative effectiveness research. Hernán MA, Hernández-Díaz S. Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Boston, MA, USA.

BACKGROUND: The intention-to-treat comparison is the primary, if not the only, analytic approach of many randomized clinical trials.

PURPOSE: To review the shortcomings of intention-to-treat analyses, and of ‘as treated’ and ‘per protocol’ analyses as commonly implemented, with an emphasis on problems that are especially relevant for comparative effectiveness research.

METHODS and RESULTS: In placebo-controlled randomized clinical trials, intention-to-treat analyses underestimate the treatment effect and are therefore nonconservative for both safety trials and noninferiority trials. In randomized clinical trials with an active comparator, intention-to-treat estimates can overestimate a treatment’s effect in the presence of differential adherence. In either case, there is no guarantee that an intention-to-treat analysis estimates the clinical effectiveness of treatment. Inverse probability weighting, g-estimation, and instrumental variable estimation can reduce the bias introduced by nonadherence and loss to follow-up in ‘as treated’ and ‘per protocol’ analyses.

LIMITATIONS: These analyse require untestable assumptions, a dose-response model, and time-varying data on confounders and adherence.

CONCLUSIONS: We recommend that all randomized clinical trials with substantial lack of adherence or loss to follow-up are analyzed using different methods. These include an intention-to-treat analysis to estimate the effect of assigned treatment and ‘as treated’ and ‘per protocol’ analyses to estimate the effect of treatment after appropriate adjustment via inverse probability weighting or g-estimation.

PMID: 21948059 [PubMed - as supplied by publisher]

3. Am J Epidemiol. 2011 Sep 20. [Epub ahead of print]

Comparison of Different Approaches to Confounding Adjustment in a Study on the Association of Antipsychotic Medication With Mortality in Older Nursing Home Patients. Huybrechts KF, Brookhart MA, Rothman KJ, Silliman RA, Gerhard T, Crystal S, Schneeweiss S.

Selective prescribing of conventional antipsychotic medication (APM) to frailer patients is thought to have led to overestimation of the association with mortality in pharmacoepidemiologic studies relying on claims data. The authors assessed the validity of different analytic techniques to address such confounding. The cohort included 82,012 persons initiating APM use after admission to a nursing home in 45 states with 2001-2005 Medicaid/Medicare data, linked to clinical data (Minimum Data Set) and institutional characteristics. The authors compared the association between APM class and 180-day mortality with multivariate outcome modeling, propensity score (PS) adjustment, and instrumental variables. The unadjusted risk difference (per 100 patients) of 10.6 (95% confidence interval (CI): 9.4, 11.7) comparing use of conventional medication with atypical APM was reduced to 7.8 (95% CI: 6.6, 9.0) and 7.0 (95% CI: 5.8, 8.2) after PS adjustment and high-dimensional PS (hdPS) adjustment, respectively. Results were similar in analyses limited to claims-based Medicaid/Medicare variables (risk difference = 8.2 for PS, 7.1 for hdPS). Instrumental-variable estimates were imprecise (risk difference = 8.8, 95% CI: -1.3, 19.0) because of the weak instrument. These results suggest that residual confounding has a relatively small impact on the effect estimate and that hdPS methods based on claims alone provide estimates at least as good as those from conventional analyses using claims enriched with clinical information.

PMID: 21934095 [PubMed - as supplied by publisher]

4. Pharmacoepidemiol Drug Saf. 2011 Sep 15. doi: 10.1002/pds.2196. [Epub ahead of print]

Study design for a comprehensive assessment of biologic safety using multiple healthcare data systems. Herrinton LJ, Curtis JR, Chen L, Liu L, Delzell E, Lewis JD, Solomon DH, Griffin MR, Ouellet-Hellstom R, Beukelman T, Grijalva CG, Haynes K, Kuriya B, Lii J, Mitchel E, Patkar N, Rassen J, Winthrop KL, Nourjah P, Saag KG. Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA. lisa.herrinton@kp.org.

BACKGROUND: Although biologic treatments have excellent efficacy for many autoimmune diseases, safety concerns persist. Understanding the absolute and comparative risks of adverse events in patient and disease subpopulations is critical for optimal prescribing of biologics.

PURPOSE: The Safety Assessment of Biologic Therapy collaborative was federally funded to provide robust estimates of rates and relative risks of adverse events among biologics users using data from national Medicaid and Medicare plus Medicaid dual-eligible programs, Tennessee Medicaid, Kaiser Permanente, and state pharmaceutical assistance programs supplementing New Jersey and Pennsylvania Medicare programs. This report describes the organizational structure of the collaborative and the study population and methods.

METHODS: This retrospective cohort study (1998-2007) examined risks of seven classes of adverse events in relation to biologic treatments prescribed for seven autoimmune diseases. Propensity scores were used to control for confounding and enabled pooling of individual-level data across data systems while concealing personal health information. Cox proportional hazard modeling was used to analyze study hypotheses.

RESULTS: The cohort was composed of 159,000 subjects with rheumatic diseases, 33,000 with psoriasis, and 46,000 with inflammatory bowel disease. This report summarizes demographic characteristics and drug exposures. Separate reports will provide outcome definitions and estimated hazard ratios for adverse events.

CONCLUSION: This comprehensive research will improve understanding of the safety of these treatments. The methods described may be useful to others planning similar evaluations. Copyright © 2011 John Wiley & Sons, Ltd.

PMID: 21919113 [PubMed - as supplied by publisher]

5. Contemp Clin Trials. 2011 Sep 6. [Epub ahead of print]

Comparison of statistical approaches for physician-randomized trials with survival outcomes. Stedman MR, Lew RA, Losina E, Gagnon DR, Solomon DH, Brookhart MA. Orthopedics and Arthritis Center for Outcomes Research, Department of Orthopedics, Brigham and Women’s Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.

This study compares methods for analyzing correlated survival data from physician-randomized trials of health care quality improvement interventions. Several proposed methods adjust for correlated survival data; however the most suitable method is unknown. Applying the characteristics of our study example, we performed three simulation studies to compare conditional, marginal, and non-parametric methods for analyzing clustered survival data. We simulated 1000 datasets using a shared frailty model with (1) fixed cluster size, (2) variable cluster size, and (3) non-lognormal random effects. Methods of analyses included: the nonlinear mixed model (conditional), the marginal proportional hazards model with robust standard errors, the clustered logrank test, and the clustered permutation test (non-parametric). For each method considered we estimated Type I error, power, mean squared error, and the coverage probability of the treatment effect estimator. We observed underestimated Type I error for the clustered logrank test. The marginal proportional hazards method performed well even when model assumptions were violated. Nonlinear mixed models were only advantageous when the distribution was correctly specified.

PMID: 21924382 [PubMed - as supplied by publisher]

CER Scan [published within the last 30 days]

1. BMC Med Res Methodol. 2011 Sep 21;11(1):132. [Epub ahead of print]

Benefits of ICU admission in critically ill patients: Whether instrumental variable methods or propensity scores should be used. Pirracchio R, Sprung C, Payen D, Chevret S.

BACKGROUND: The assessment of the causal effect of Intensive Care Unit (ICU) admission generally involves usual observational designs and thus requires controlling for confounding variables. Instrumental variable analysis is an econometric technique that allows causal inferences of the effectiveness of some treatments during situations to be made when a randomized trial has not been or cannot be conducted. This technique relies on the existence of one variable or “instrument” that is supposed to achieve similar observations with a different treatment for “arbitrary” reasons, thus inducing substantial variation in the treatment decision with no direct effect on the outcome. The objective of the study was to assess the benefit in terms of hospital mortality of ICU admission in a cohort of patients proposed for ICU admission (ELDICUS cohort).

METHODS: Using this cohort of 8,201 patients triaged for ICU (including 6,752 (82.3%) patients admitted), the benefit of ICU admission was evaluated using 3 different approaches: instrumental variables, standard regression and propensity score matched analyses. We further evaluated the results obtained using different instrumental variable methods that have been proposed for dichotomous outcomes.

RESULTS: The physician’s main specialization was found to be the best instrument. All instrumental variable models adequately reduced baseline imbalances, but failed to show a significant effect of ICU admission on hospital mortality, with confidence intervals far higher than those obtained in standard or propensity-based analyses.

CONCLUSIONS: Instrumental variable methods offer an appealing alternative to handle the selection bias related to nonrandomized designs, especially when the presence of significant unmeasured confounding is suspected. Applied to the ELDICUS database, this analysis failed to show any significant beneficial effect of ICU admission on hospital mortality. This result could be due to the lack of statistical power of these methods.

PMID: 21936926 [PubMed - as supplied by publisher]

Free Full Text: http://www.biomedcentral.com/content/pdf/1471-2288-11-132.pdf

2. BMC Med Res Methodol. 2011 Sep 19;11(1):129.

Imputation of missing values of tumour stage in population-based cancer registration. Eisemann N, Waldmann A, Katalinic A. Institute of Cancer Epidemiology, University Luebeck, Ratzeburger Allee 160 (Haus 50), 23562 Luebeck, Germany. nora.eisemann@uksh.de.

BACKGROUND: Missing data on tumour stage information is a common problem in population-based cancer registries. Statistical analyses on the level of tumour stage may be biased, if no adequate method for handling of missing data is applied. In order to determine a useful way to treat missing data on tumour stage, we examined different imputation models for multiple imputation with chained equations for analysing the stage-specific numbers of cases of malignant melanoma and female breast cancer.

METHODS: This analysis was based on the malignant melanoma data set and the female breast cancer data set of the cancer registry Schleswig-Holstein, Germany. The cases with complete tumour stage information were extracted and their stage information partly removed according to a MAR missingness-pattern, resulting in five simulated data sets for each cancer entity. The missing tumour stage values were then treated with multiple imputation with chained equations, using polytomous regression, predictive mean matching, random forests and proportional sampling as imputation models. The estimated tumour stages, stage-specific numbers of cases and survival curves after multiple imputation were compared to the observed ones.

RESULTS: The amount of missing values for malignant melanoma was too high to estimate a reasonable number of cases for each UICC stage. However, multiple imputation of missing stage values led to stage-specific numbers of cases of T-stage for malignant melanoma as well as T- and UICC-stage for breast cancer close to the observed numbers of cases. The observed tumour stages on the individual level, the stage-specific numbers of cases and the observed survival curves were best met with polytomous regression or predictive mean matching but not with random forest or proportional sampling as imputation models.

CONCLUSIONS: This limited simulation study indicates that multiple imputation with chained equations is an appropriate technique for dealing with missing information on tumour stage in population-based cancer registries, if the amount of unstaged cases is on a reasonable level.

PMID: 21929796 [PubMed - as supplied by publisher]

Free Full Text: http://www.biomedcentral.com/content/pdf/1471-2288-11-129.pdf

3. Med Care. 2011 Oct;49(10):940-7.

The Mortality Risk Score and the ADG Score: Two Points-Based Scoring Systems for the Johns Hopkins Aggregated Diagnosis Groups to Predict Mortality in a General Adult Population Cohort in Ontario, Canada. Austin PC, Walraven C. *Institute for Clinical Evaluative Sciences, Toronto, Ontario †Department of Health Management, Policy and Evaluation ‡Dalla Lana School of Public Health, University of Toronto §Ottawa Hospital Research Institute Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada.

BACKGROUND: Logistic regression models that incorporated age, sex, and indicator variables for the Johns Hopkins’ Aggregated Diagnosis Groups (ADGs) categories have been shown to accurately predict all-cause mortality in adults.

OBJECTIVES: To develop 2 different point-scoring systems using the ADGs. The Mortality Risk Score (MRS) collapses age, sex, and the ADGs to a single summary score that predicts the annual risk of all-cause death in adults. The ADG Score derives weights for the individual ADG diagnosis groups.

RESEARCH DESIGN: Retrospective cohort constructed using population-based administrative data.

PARTICIPANTS: All 10,498,413 residents of Ontario, Canada, between the age of 20 and 100 years who were alive on their birthday in 2007, participated in this study. Participants were randomly divided into derivation and validation samples.

MEASURES: Death within 1 year.

RESULTS: In the derivation cohort, the MRS ranged from -21 to 139 (median value 29, IQR 17 to 44). In the validation group, a logistic regression model with the MRS as the sole predictor significantly predicted the risk of 1-year mortality with a c-statistic of 0.917. A regression model with age, sex, and the ADG Score has similar performance. Both methods accurately predicted the risk of 1-year mortality across the 20 vigintiles of risk.

CONCLUSIONS: The MRS combined values for a person’s age, sex, and the John Hopkins ADGs to accurately predict 1-year mortality in adults. The ADG Score is a weighted score representing the presence or absence of the 32 ADG diagnosis groups. These scores will facilitate health services researchers conducting risk adjustment using administrative health care databases.

PMID: 21921849 [PubMed - in process]

4. Ann Epidemiol. 2011 Oct;21(10):780-6.

Mixture analysis of heterogeneous physical activity outcomes. Lee AH, Xiang L. Department of Epidemiology and Biostatistics, School of Public Health, Curtin University, Perth, WA, Australia.

PURPOSE: The health benefits of physical activity (PA) are well established. PA outcomes, being semicontinuous in nature, often exhibit a large portion of zero values together with continuous positive values that are right-skewed. We propose a novel two-part mixture regression model with random effects to characterize heterogeneity of the clustered PA data.

METHODS: In the binary part, the odds of PA participation are modeled with the use of a logistic mixed regression model. In the continuous part, the PA intensity conditional on those individuals engaging in PA is assessed by a gamma mixture regression model. Random effects are incorporated within the two parts to account for correlation of the observations.

RESULTS: Model fitting and inference are performed through the Gaussian quadrature technique, which is implemented conveniently in the SAS PROC NLMIXED. The development of mixture methodology for analyzing PA is motivated by a study of PA in the daily life of patients with chronic obstructive pulmonary disease.

CONCLUSIONS: The findings demonstrate the usefulness of the mixture analysis, which enables the separate identification of pertinent factors affecting PA participation and PA intensity for different patient subgroups.

PMID: 21684174 [PubMed - in process]

Additional Article of Interest [published within the last 90 days]:

1. Am J Prev Med 2011;40(6):637–644.

A Proposal to Speed Translation of Healthcare Research Into Practice: Dramatic Change Is Needed. Kessler R, Glasgow RE.

Efficacy trials have generated interventions to improve health behaviors and biomarkers. However, these efforts have had limited impact on practice and policy. It is suggested that key methodologic and contextual issues have contributed to this state of affairs. Current research paradigms generally have not provided the answers needed for more probable and more rapid translation. A major shift is proposed to produce research with more rapid clinical, public health, and policy impact. Copyright © 2011 American Journal of Preventive Medicine. All rights reserved.

PMID: 21565657 [PubMed - indexed for MEDLINE]

September 2011

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CER Scan [Epub ahead of print]

1. Biostatistics. 2011 Aug 18. [Epub ahead of print]

A robust method using propensity score stratification for correcting verification bias for binary tests. He H, McDermott MP. Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA. mikem@bst.rochester.edu.

Sensitivity and specificity are common measures of the accuracy of a diagnostic test. The usual estimators of these quantities are unbiased if data on the diagnostic test result and the true disease status are obtained from all subjects in an appropriately selected sample. In some studies, verification of the true disease status is performed only for a subset of subjects, possibly depending on the result of the diagnostic test and other characteristics of the subjects. Estimators of sensitivity and specificity based on this subset of subjects are typically biased; this is known as verification bias. Methods have been proposed to correct verification bias under the assumption that the missing data on disease status are missing at random (MAR), that is, the probability of missingness depends on the true (missing) disease status only through the test result and observed covariate information. When some of the covariates are continuous, or the number of covariates is relatively large, the existing methods require parametric models for the probability of disease or the probability of verification (given the test result and covariates), and hence are subject to model misspecification. We propose a new method for correcting verification bias based on the propensity score, defined as the predicted probability of verification given the test result and observed covariates. This is estimated separately for those with positive and negative test results. The new method classifies the verified sample into several subsamples that have homogeneous propensity scores and allows correction for verification bias. Simulation studies demonstrate that the new estimators are more robust to model misspecification than existing methods, but still perform well when the models for the probability of disease and probability of verification are correctly specified.

PMID: 21856650 [PubMed - as supplied by publisher]

2. Prev Med. 2011 Aug 17. [Epub ahead of print]

Null misinterpretation in statistical testing and its impact on health risk assessment. Greenland S.

Statistical methods play a pivotal role in health risk assessment, but not always an enlightened one. Problems well known to academics are frequently overlooked in crucial nonacademic venues such as litigation, even though those venues can have profound impacts on population health and medical practice. Statisticians have focused heavily on how statistical significance overstates evidence against null hypotheses, but less on how statistical nonsignificance does not correspond to evidence for the null. I thus present an example of a highly credentialed statistical expert conflating high “nonsignificance” with strong support for the null, via misinterpretation of a P-value as a posterior probability of the null hypothesis. Reverse-Bayes analyses reveal that nearly all the support for the null claimed by the expert must have come from the expert’s prior, rather than the data, and that there was no background data that could support a strong prior. The example illustrates how carelessness about the actual meaning of P-values and confidence limits allow extremely biased prior opinions (including null-spiked opinions) to be presented as if they were objective inferences from the data.

PMID: 21871481 [PubMed - as supplied by publisher]

3. J Clin Epidemiol. 2011 Aug 11. [Epub ahead of print]

The “best balance” allocation led to optimal balance in cluster-controlled trials. de Hoop E, Teerenstra S, van Gaal BG, Moerbeek M, Borm GF. Department of Epidemiology, Biostatistics and HTA, 133, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands.

OBJECTIVE: Balance of prognostic factors between treatment groups is desirable because it improves the accuracy, precision, and credibility of the results. In cluster-controlled trials, imbalance can easily occur by chance when the number of cluster is small. If all clusters are known at the start of the study, the “best balance” allocation method (BB) can be used to obtain optimal balance. This method will be compared with other allocation methods.

STUDY DESIGN AND SETTING: We carried out a simulation study to compare the balance obtained with BB, minimization, unrestricted randomization, and matching for four to 20 clusters and one to five categorical prognostic factors at cluster level.

RESULTS: BB resulted in a better balance than randomization in 13-100% of the situations, in 0-61% for minimization, and in 0-88% for matching. The superior performance of BB increased as the number of clusters and/or the number of factors increased.

CONCLUSION: BB results in a better balance of prognostic factors than randomization, minimization, stratification, and matching in most situations. Furthermore, BB cannot result in a worse balance of prognostic factors than the other methods.

PMID: 21840173 [PubMed - as supplied by publisher]

4. Stat Med. 2011 Aug 4. doi: 10.1002/sim.4322. [Epub ahead of print]

Subgroup identification from randomized clinical trial data. Foster JC, Taylor JM, Ruberg SJ. Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.

We consider the problem of identifying a subgroup of patients who may have an enhanced treatment effect in a randomized clinical trial, and it is desirable that the subgroup be defined by a limited number of covariates. For this problem, the development of a standard, pre-determined strategy may help to avoid the well-known dangers of subgroup analysis. We present a method developed to find subgroups of enhanced treatment effect. This method, referred to as ‘Virtual Twins’, involves predicting response probabilities for treatment and control ‘twins’ for each subject. The difference in these probabilities is then used as the outcome in a classification or regression tree, which can potentially include any set of the covariates. We define a measure Q(Â) to be the difference between the treatment effect in estimated subgroup  and the marginal treatment effect. We present several methods developed to obtain an estimate of Q(Â), including estimation of Q(Â) using estimated probabilities in the original data, using estimated probabilities in newly simulated data, two cross-validation-based approaches, and a bootstrap-based bias-corrected approach. Results of a simulation study indicate that the Virtual Twins method noticeably outperforms logistic regression with forward selection when a true subgroup of enhanced treatment effect exists. Generally, large sample sizes or strong enhanced treatment effects are needed for subgroup estimation. As an illustration, we apply the proposed methods to data from a randomized clinical trial. Copyright © 2011 John Wiley & Sons, Ltd.

PMID: 21815180 [PubMed - as supplied by publisher]

5. Pharmacoepidemiol Drug Saf. 2011 Aug 2. doi: 10.1002/pds.2205. [Epub ahead of print]

Record linkage for pharmacoepidemiological studies in cancer patients. Herk-Sukel MP, Lemmens VE, Poll-Franse LV, Herings RM, Coebergh JW. PHARMO Institute for Drug Outcomes Research, Utrecht, the Netherlands. myrthe.van.herk@pharmo.nl.

BACKGROUND: An increasing need has developed for the post-approval surveillance of (new) anti-cancer drugs by means of pharmacoepidemiology and outcomes research in the area of oncology.

OBJECTIVES: To create an overview that makes researchers aware of the available database linkages in Northern America and Europe which facilitate pharmacoepidemiology and outcomes research in cancer patients.

METHODS: In addition to our own database, i.e. the Eindhoven Cancer Registry (ECR) linked to the PHARMO Record Linkage System, we considered database linkages between a population-based cancer registry and an administrative healthcare database that at least contains information on drug use and offers a longitudinal perspective on healthcare utilization. Eligible database linkages were limited to those that had been used in multiple published articles in English language included in Pubmed. The HMO Cancer Research Network (CRN) in the US was excluded from this review, as an overview of the linked databases participating in the CRN is already provided elsewhere. Researchers who had worked with the data resources included in our review were contacted for additional information and verification of the data presented in the overview.

RESULTS: The following database linkages were included: the Surveillance, Epidemiology, and End-Results-Medicare; cancer registry data linked to Medicaid; Canadian cancer registries linked to population-based drug databases; the Scottish cancer registry linked to the Tayside drug dispensing data; linked databases in the Nordic Countries of Europe: Norway, Sweden, Finland and Denmark; and the ECR-PHARMO linkage in the Netherlands. Descriptives of the included database linkages comprise population size, generalizability of the population, year of first data availability, contents of the cancer registry, contents of the administrative healthcare database, the possibility to select a cancer-free control cohort, and linkage to other healthcare databases.

CONCLUSIONS: The linked databases offer a longitudinal perspective, allowing for observations of health care utilization before, during, and after cancer diagnosis. They create new powerful data resources for the monitoring of post-approval drug utilization, as well as a framework to explore the cost-effectiveness of new, often expensive, anti-cancer drugs as used in everyday practice. Copyright © 2011 John Wiley & Sons, Ltd.

PMID: 21812067 [PubMed - as supplied by publisher]

CER Scan [published within the last 30 days]

1. JAMA. 2011 Aug 24;306(8):848-55.

Automated identification of postoperative complications within an electronic medical record using natural language processing. Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, Dittus RS, Rosen AK, Elkin PL, Brown SH, Speroff T. Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA. harvey.j.murff@vanderbilt.edu

Comment in: JAMA. 2011 Aug 24;306(8):880-1.

CONTEXT: Currently most automated methods to identify patient safety occurrences rely on administrative data codes; however, free-text searches of electronic medical records could represent an additional surveillance approach. OBJECTIVE: To evaluate a natural language processing search-approach to identify postoperative surgical complications within a comprehensive electronic medical record.

DESIGN, SETTING, AND PATIENTS: Cross-sectional study involving 2974 patients undergoing inpatient surgical procedures at 6 Veterans Health Administration (VHA) medical centers from 1999 to 2006.

MAIN OUTCOME MEASURES: Postoperative occurrences of acute renal failure requiring dialysis, deep vein thrombosis, pulmonary embolism, sepsis, pneumonia, or myocardial infarction identified through medical record review as part of the VA Surgical Quality Improvement Program. We determined the sensitivity and specificity of the natural language processing approach to identify these complications and compared its performance with patient safety indicators that use discharge coding information. RESULTS: The proportion of postoperative events for each sample was 2% (39 of 1924) for acute renal failure requiring dialysis, 0.7% (18 of 2327) for pulmonary embolism, 1% (29 of 2327) for deep vein thrombosis, 7% (61 of 866) for sepsis, 16% (222 of 1405) for pneumonia, and 2% (35 of 1822) for myocardial infarction. Natural language processing correctly identified 82% (95% confidence interval [CI], 67%-91%) of acute renal failure cases compared with 38% (95% CI, 25%-54%) for patient safety indicators. Similar results were obtained for venous thromboembolism (59%, 95% CI, 44%-72% vs 46%, 95% CI, 32%-60%), pneumonia (64%, 95% CI, 58%-70% vs 5%, 95% CI, 3%-9%), sepsis (89%, 95% CI, 78%-94% vs 34%, 95% CI, 24%-47%), and postoperative myocardial infarction (91%, 95% CI, 78%-97%) vs 89%, 95% CI, 74%-96%). Both natural language processing and patient safety indicators were highly specific for these diagnoses.

CONCLUSION: Among patients undergoing inpatient surgical procedures at VA medical centers, natural language processing analysis of electronic medical records to identify postoperative complications had higher sensitivity and lower specificity compared with patient safety indicators based on discharge coding.

PMID: 21862746 [PubMed - indexed for MEDLINE]

2. JAMA. 2011 Aug 17;306(7):709; author reply 709-10.

Efficacy research and unanswered clinical questions. Vohra S, Shamseer L, Sampson M.

Comment on: JAMA. 2011 May 18;305(19):2005-6.

PMID: 21846851 [PubMed - indexed for MEDLINE]

3. Pharmacoepidemiol Drug Saf. 2011 Aug;20(8):858-65. doi: 10.1002/pds.2160. Epub 2011 Jun 13.

Why do covariates defined by International Classification of Diseases codes fail to remove confounding in pharmacoepidemiologic studies among seniors? Jackson ML, Nelson JC, Jackson LA. Group Health Research Institute, Seattle, WA, USA. jackson.ml@ghc.org.

PURPOSE: The common practice of using administrative diagnosis codes as the sole source of data on potential confounders in pharmacoepidemiologic studies has been shown to leave substantial residual confounding. We explored reasons why adjustment for comorbid illness defined from International Classification of Diseases (ICD) codes fails to remove confounding.

METHODS: We used data from a case-control study among immunocompetent seniors enrolled in Group Health to estimate bias in the estimated association between receipt of influenza vaccine and the risk of community-acquired pneumonia during non-influenza control periods and to estimate the effects of adjusting for comorbid illnesses defined from either ICD codes or the medical record. We also estimated the accuracy of ICD codes for identifying comorbid illnesses compared with the gold standard of medical record review.

RESULTS: Sensitivity of ICD codes for illnesses recorded in the medical record ranged from 59 to 97% (median, 76%). Strong confounding was present in the vaccine/pneumonia association, as evidenced by the non-null odds ratio of 0.60 (95% confidence interval, 0.38-0.95) during this control period. Adjusting for the presence/absence of comorbid illnesses defined from either medical record review (odds ratio, 0.73) or from ICD codes (odds ratio, 0.68) left considerable residual confounding.

CONCLUSIONS: ICD codes may fail to control for confounding because they often lack sensitivity for detecting comorbid illnesses and because measures of the presence/absence of comorbid illnesses may be insufficient to remove confounding. These findings call for caution in the use of ICD codes to control for confounding. Copyright © 2011 John Wiley & Sons, Ltd.

PMID: 21671442 [PubMed - in process]

4. J Clin Epidemiol. 2011 Aug;64(8):821-9. Epub 2010 Dec 30.

Development and use of reporting guidelines for assessing the quality of validation studies of health administrative data. Benchimol EI, Manuel DG, To T, Griffiths AM, Rabeneck L, Guttmann A. The Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. ebenchimol@cheo.on.ca

BACKGROUND AND OBJECTIVES: Validation of health administrative data for identifying patients with different health states (diseases and conditions) is a research priority, but no guidelines exist for ensuring quality. We created reporting guidelines for studies validating administrative data identification algorithms and used them to assess the quality of reporting of validation studies in the literature.

METHODS: Using Standards for Reporting of Diagnostic accuracy (STARD) criteria as a guide, we created a 40-item checklist of items with which identification accuracy studies should be reported. A systematic review identified studies that validated identification algorithms using administrative data. We used the checklist to assess the quality of reporting.

RESULTS: In 271 included articles, goals and data sources were well reported but few reported four or more statistical estimates of accuracy (36.9%). In 65.9% of studies reporting positive predictive value (PPV)/negative predictive value (NPV), the prevalence of disease in the validation cohort was higher than in the administrative data, potentially falsely elevating predictive values. Subgroup accuracy (53.1%) and 95% confidence intervals for accuracy measures (35.8%) were also underreported.

CONCLUSIONS: The quality of studies validating health states in the administrative data varies, with significant deficits in reporting of markers of diagnostic accuracy, including the appropriate estimation of PPV and NPV. These omissions could lead to misclassification bias and incorrect estimation of incidence and health services utilization rates. Use of a reporting checklist, such as the one created for this study by modifying the STARD criteria, could improve the quality of reporting of validation studies, allowing for accurate application of algorithms, and interpretation of research using health administrative data.

PMID: 21194889 [PubMed - indexed for MEDLINE]

SEPTEMBER THEME: Application of Propensity Scores in CER of Surgical Interventions (This is a cross-section of studies published within the last year that demonstrate the level discussion in the field. The Methods Center does not necessarily endorse the studies’ methodology)

1. Arch Surg. 2010 Oct;145(10):939-45.

Introduction to propensity scores: A case study on the comparative effectiveness of laparoscopic vs open appendectomy. Hemmila MR, Birkmeyer NJ, Arbabi S, Osborne NH, Wahl WL, Dimick JB. Department of Surgery, University of Michigan Medical School, Ann Arbor, 48109-5033, USA. mhemmila@umich.edu

Comment in Arch Surg. 2010 Oct;145(10):945-6.

OBJECTIVE: To demonstrate the use of propensity scores to evaluate the comparative effectiveness of laparoscopic and open appendectomy.

DESIGN: Retrospective cohort study.

SETTING: Academic and private hospitals.

PATIENTS: All patients undergoing open or laparoscopic appendectomy (n = 21 475) in the Public Use File of the American College of Surgeons National Surgical Quality Improvement Program were included in the study. We first evaluated the surgical approach (laparoscopic vs open) using multivariate logistic regression. We next generated propensity scores and compared outcomes for open and laparoscopic appendectomy in a 1:1 matched cohort. Covariates in the model for propensity scores included comorbidities, age, sex, race, and evidence of perforation.

MAIN OUTCOME MEASURES: Patient morbidity and mortality, rate of return to operating room, and hospital length of stay.

RESULTS: Twenty-eight percent of patients underwent open appendectomy, and 72% had a laparoscopic approach; 33% (open) vs 14% (laparoscopic) had evidence of a ruptured appendix. In the propensity-matched cohort, there was no difference in mortality (0.3% vs 0.2%), reoperation (1.8% vs 1.5%), or incidence of major complications (5.9% vs 5.4%) between groups. Patients undergoing laparoscopic appendectomy experienced fewer wound infections (odds ratio [OR], 0.4; 95% confidence interval [CI], 0.3-0.5) and fewer episodes of sepsis (0.8; 0.6-1.0) but had a greater risk of intra-abdominal abscess (1.7; 1.3-2.2). An analysis using multivariate adjustment resulted in similar findings.

CONCLUSIONS: After accounting for patient severity, open and laparoscopic appendectomy had similar clinical outcomes. In this case study, propensity score methods and multivariate adjustment yielded nearly identical results.

PMID: 20956761 [PubMed - indexed for MEDLINE]

2. Arch Surg. 2011 Jul;146(7):887-8.

Propensity score methods: setting the score straight. Mayo SC, Pawlik TM. Department of Surgery, Johns Hopkins University, 600 N Wolfe St, Blalock 655, Baltimore, MD 21287. tpawlik1@jhmi.edu.

PMID: 21768443 [PubMed - in process]

3. J Thorac Cardiovasc Surg. 2011 Aug 13. [Epub ahead of print]

On-pump and off-pump coronary artery bypass grafting in patients with left main stem disease: A propensity score analysis. Murzi M, Caputo M, Aresu G, Duggan S, Miceli A, Glauber M, Angelini GD. Bristol Heart Institute, University of Bristol, Bristol, UK.

OBJECTIVE: This study compared safety and efficacy between off-pump coronary artery bypass grafting (OPCAB), a relatively new technique, and conventional on-pump coronary artery bypass grafting (CCAB) in patients with left main stem disease.

METHODS: In a retrospective, observational, cohort study of prospectively collected data on 2375 consecutive patients with left main stem disease undergoing isolated CABG (1297 OPCAB, 1078 CCAB) between April 1996 and December 2009 at the Bristol Heart Institute, 548 patients undergoing OPCAB were matched with 548 patients undergoing CCAB by propensity score.

RESULTS: After propensity matching, groups were comparable in preoperative characteristics. Relative to CCAB, OPCAB was associated with lower in-hospital mortality (0.5% vs 2.9%; P = .001), incidence of stroke (0% vs 0.9%; P = .02), postoperative renal dysfunction (4.9% vs 10.8%; P = .001), pulmonary complications (10.2% vs 16.6%; P = .002), and infectious complications (3.5% vs 6.2%; P = .03). The OPCAB group received fewer grafts than did the CCAB group (2.7 ± 0.7 vs 3 ± 0.7; P = .001) and had a lower rate of complete revascularization (88.3% vs 92%; P = .04). In multivariable analysis, cardiopulmonary bypass was confirmed to be an independent predictor of in-hospital mortality (odds ratio, 5.74; P = .001). Survivals at 1, 5, and 10 years were similar between groups (OPCAB, 96.8%, 87.3%, and 71.7%; CCAB, 96.8%, 88.6%, and 69.8%).

CONCLUSIONS: OPCAB in patients with left main stem disease is a safe procedure with reduced early morbidity and mortality and similar long-term survival to conventional on-pump revascularization.

PMID: 21843893 [PubMed - as supplied by publisher]

4. J Thorac Cardiovasc Surg. 2011 Jun 16. [Epub ahead of print]

Results of matching valve and root repair to aortic valve and root pathology.Svensson LG, Batizy LH, Blackstone EH, Marc Gillinov A, Moon MC, D’Agostino RS, Nadolny EM, Stewart WJ, Griffin BP, Hammer DF, Grimm R, Lytle BW. Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic, Cleveland, Ohio.

OBJECTIVE: For patients with aortic root pathology and aortic valve regurgitation, aortic valve replacement is problematic because no durable bioprosthesis exists, and mechanical valves require lifetime anticoagulation. This study sought to assess outcomes of combined aortic valve and root repair, including comparison with matched bioprosthesis aortic valve replacement.

METHODS: From November 1990 to January 2005, 366 patients underwent modified David reimplantation (n = 72), root remodeling (n = 72), or valve repair with sinotubular junction tailoring (n = 222). Active follow-up was 99% complete, with a mean of 5.6 ± 4.0 years (maximum 17 years); follow-up for vital status averaged 8.5 ± 3.6 years (maximum 19 years). Propensity-adjusted models were developed for fair comparison of outcomes.

RESULTS: Thirty-day and 5-, 10-, and 15-year survivals were 98%, 86%, 74%, and 58%, respectively, similar to that of the US matched population and better than that after bioprosthesis aortic valve replacement. Propensity-score-adjusted survival was similar across procedures (P > .3). Freedom from reoperation at 30 days and 5 and 10 years was 99%, 92%, and 89%, respectively, and was similar across procedures (P > .3) after propensity-score adjustment. Patients with tricuspid aortic valves were more likely to be free of reoperation than those with bicuspid valves at 10 years (93% vs 77%, P = .002), equivalent to bioprosthesis aortic valve replacement and superior after 12 years. Bioprostheses increasingly deteriorated after 7 years, and hazard functions for reoperation crossed at 7 years.

CONCLUSIONS: Valve preservation (rather than replacement) and matching root procedures have excellent early and long-term results, with increasing survival benefit at 7 years and fewer reoperations by 12 years. We recommend this procedure for experienced surgical teams.

PMID: 21683965 [PubMed - as supplied by publisher]

5. Ann Surg. 2011 Feb;253(2):385-92.

Can the impact of change of surgical teams in cardiovascular surgery be measured by operative mortality or morbidity? A propensity adjusted cohort comparison. Brown ML, Parker SE, Quiñonez LG, Li Z, Sundt TM. Division of Anesthesiology and Pain Medicine, University of Alberta, Edmonton, AB, Canada.

OBJECTIVE: Our objective was to examine the impact of team changeover and unfamiliar teams in cardiovascular surgery on traditional clinical outcome measures.

BACKGROUND: The importance of teamwork in the operating room is increasingly being appreciated, but the impact on more traditional outcome measures is unclear.

METHODS: Elective or urgent cardiovascular procedures were divided into categories: team D (patients who had an operation with a day team); team E (patients who had an operation with an evening team); team C (patients who had an operation which included changeover between a day and evening team). Comparison groups were adjusted using propensity scores.

RESULTS: We identified 6698 patients who met inclusion criteria (team D, n =3781; team E, n = 518; team C, n = 2399). After propensity score adjustment,there was an increased skin–skin time of 28 minutes in team C when compared with team D (P < 0.001) and of 21 minutes when compared with team E (P <0.001). There were also more episodes of septicemia among team C patients(OR 1.85, P = 0.013) when compared with team D. Patients operated by a day team had a statistically significantly lower number of ventilated hours and shorter hospital length of stay when compared with team E and team C (P < 0.001 and P < 0.001, respectively). There was no difference between teams in operative death, reoperation for bleeding, blood transfusion, renal failure/dialysis, neurologic events, or deep/superficial wound infections.

CONCLUSIONS: The change in operating room personnel from the day team to the evening team added significant length to the total operating department time in cardiovascular surgery; however, its impact on most traditional outcome measures was difficult to demonstrate. More sensitive outcome measures may be required to assess the impact of teamwork interventions.

PMID: 21173693 [PubMed - indexed for MEDLINE]

6. J Thorac Cardiovasc Surg. 2011 Jan;141(1):72-80.e1-4. Epub 2010 Nov 19.

Robotic repair of posterior mitral valve prolapse versus conventional approaches: potential realized. Mihaljevic T, Jarrett CM, Gillinov AM, Williams SJ, DeVilliers PA, Stewart WJ, Svensson LG, Sabik JF 3rd, Blackstone EH. Department of Thoracic and Cardiovascular Surgery, Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA. mihaljt@ccf.org

OBJECTIVE: Robotic mitral valve repair is the least invasive approach to mitral valve repair, yet there are few data comparing its outcomes with those of conventional approaches. Therefore, we compared outcomes of robotic mitral valve repair with those of complete sternotomy, partial sternotomy, and right mini-anterolateral thoracotomy.

METHODS: From January 2006 to January 2009, 759 patients with degenerative mitral valve disease and posterior leaflet prolapse underwent primary isolated mitral valve surgery by complete sternotomy (n = 114), partial sternotomy (n = 270), right mini-anterolateral thoracotomy (n = 114), or a robotic approach (n = 261). Outcomes were compared on an intent-to-treat basis using propensity-score matching.

RESULTS: Mitral valve repair was achieved in all patients except 1 patient in the complete sternotomy group. In matched groups, median cardiopulmonary bypass time was 42 minutes longer for robotic than complete sternotomy, 39 minutes longer than partial sternotomy, and 11 minutes longer than right mini-anterolateral thoracotomy (P < .0001); median myocardial ischemic time was 26 minutes longer than complete sternotomy and partial sternotomy, and 16 minutes longer than right mini-anterolateral thoracotomy (P < .0001). Quality of mitral valve repair was similar among matched groups (P = .6, .2, and .1, respectively). There were no in-hospital deaths. Neurologic, pulmonary, and renal complications were similar among groups (P > .1). The robotic group had the lowest occurrences of atrial fibrillation and pleural effusion, contributing to the shortest hospital stay (median 4.2 days), 1.0, 1.6, and 0.9 days shorter than for complete sternotomy, partial sternotomy, and right mini-anterolateral thoracotomy (all P < .001), respectively.

CONCLUSIONS: Robotic repair of posterior mitral valve leaflet prolapse is as safe and effective as conventional approaches. Technical complexity and longer operative times for robotic repair are compensated for by lesser invasiveness and shorter hospital stay.

PMID: 21093881 [PubMed - indexed for MEDLINE]

7. J Urol. 2011 Jan;185(1):111-5. Epub 2010 Nov 12.

Comparative effectiveness of perineal versus retropubic and minimally invasive radical prostatectomy. Prasad SM, Gu X, Lavelle R, Lipsitz SR, Hu JC. Division of Urologic Surgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA. sprasad1@bsd.surgery.uchicago.edu

Comment in

J Urol. 2011 Jul;186(1):350-1; author reply 351.

J Urol. 2011 Jul;186(1):351; author reply 351-2.

PURPOSE: While perineal radical prostatectomy has been largely supplanted by retropubic and minimally invasive radical prostatectomy, it was the predominant surgical approach for prostate cancer for many years. In our population based study we compared the use and outcomes of perineal radical prostatectomy vs retropubic and minimally invasive radical prostatectomy.

MATERIALS AND METHODS: We identified men diagnosed with prostate cancer from 2003 to 2005 who underwent perineal (452), minimally invasive (1,938) and retropubic (6,899) radical prostatectomy using Surveillance, Epidemiology and End Results-Medicare linked data through 2007. We compared postoperative 30-day and anastomotic stricture complications, incontinence and erectile dysfunction, and cancer therapy (hormonal therapy and/or radiotherapy).

RESULTS: Perineal radical prostatectomy comprised 4.9% of radical prostatectomies during our study period and use decreased with time. On propensity score adjusted analysis men who underwent perineal vs retropubic radical prostatectomy had shorter hospitalization (median 2 vs 3 days, p < 0.001), received fewer heterologous transfusions (7.2% vs 20.8%, p < 0.001) and required less additional cancer therapy (4.9% vs 6.9%, p = 0.020). When comparing perineal vs minimally invasive radical prostatectomy men who underwent the former required more heterologous transfusions (7.2% vs 2.7%, p = 0.018) but experienced fewer miscellaneous medical complications (5.3% vs 10.0%, p = 0.045) and erectile dysfunction procedures (1.4 vs 2.3/100 person-years, p = 0.008). The mean and median expenditure for perineal radical prostatectomy in the first 6 months postoperatively was $1,500 less than for retropubic or minimally invasive radical prostatectomy (p < 0.001).

CONCLUSIONS: Men who undergo perineal vs retropubic and minimally invasive radical prostatectomy experienced favorable outcomes associated with lower expenditure. Urologists may be abandoning an underused but cost-effective surgical approach that compares favorably with its successors.

PMID: 21074198 [PubMed - indexed for MEDLINE]

8. Ann Surg. 2010 Nov;252(5):765-73.

Infrapopliteal percutaneous transluminal angioplasty versus bypass surgery as first-line strategies in critical leg ischemia: a propensity score analysis. Söderström MI, Arvela EM, Korhonen M, Halmesmäki KH, Albäck AN, Biancari F, Lepäntalo MJ, Venermo MA. Department of Vascular Surgery, Helsinki University Central Hospital, Helsinki, Finland.

INTRODUCTION: Recently, endovascular revascularization (percutaneous transluminal angioplasty [PTA]) has challenged surgery as a method for the salvage of critically ischemic legs (CLI). Comparison of surgical and endovascular techniques in randomized controlled trials is difficult because of differences in patient characteristics. To overcome this problem, we adjusted the differences by using propensity score analysis.

MATERIALS AND METHODS: The study cohort comprised 1023 patients treated for CLI with 262 endovascular and 761 surgical revascularization procedures to their crural or pedal arteries. A propensity score was used for adjustment in multivariable analysis, for stratification, and for one-to-one matching.

RESULTS: In the overall series, PTA and bypass surgery achieved similar 5-year leg salvage (75.3% vs 76.0%), survival (47.5% vs 43.3%), and amputation-free survival (37.7% vs 37.3%) rates and similar freedom from any further revascularization (77.3% vs 74.4%), whereas freedom from surgical revascularization was higher after bypass surgery (94.3% vs 86.2%, P < 0.001). In propensity-score-matched pairs, outcomes did not differ, except for freedom from surgical revascularization, which was significantly higher in the bypass surgery group (91.4% vs 85.3% at 5 years, P = 0.045). In a subgroup of patients who underwent isolated infrapopliteal revascularization, PTA was associated with better leg salvage (75.5% vs 68.0%, P = 0.042) and somewhat lower freedom from surgical revascularization (78.8% vs 85.2%, P = 0.17). This significant difference in the leg salvage rate was also observed after adjustment for propensity score (P = 0.044), but not in propensity-score-matched pairs (P = 0.12).

CONCLUSIONS: When feasible, infrapopliteal PTA as a first-line strategy is expected to achieve similar long-term results to bypass surgery in CLI when redo surgery is actively utilized.

PMID: 21037432 [PubMed - indexed for MEDLINE]

August 2011

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CER Scan [Epub ahead of print]

1. Pharmacoepidemiol Drug Saf. 2011 Jul 29. doi: 10.1002/pds.2188. [Epub ahead of print]

Measuring balance and model selection in propensity score methods. Belitser SV, Martens EP, Pestman WR, Groenwold RH, de Boer A, Klungel OH. Department of Pharmacoepidemiology and Pharmacotherapy, Utrecht Institute ofPharmaceutical Sciences, Utrecht University, Utrecht, Netherlands.

PURPOSE: Propensity score (PS) methods focus on balancing confounders between groups to estimate an unbiased treatment or exposure effect. However, there is lack of attention in actually measuring, reporting and using the information on balance, for instance for model selection. We propose to use a measure for balance in PS methods and describe several of such measures: the overlapping coefficient, the Kolmogorov-Smirnov distance, and the Lévy distance.

METHODS: We performed simulation studies to estimate the association between these three and several mean based measures for balance and bias (i.e., discrepancy between the true and the estimated treatment effect).

RESULTS: For large sample sizes (n=2000) the average Pearson’s correlation coefficients between bias and Kolmogorov-Smirnov distance (r=0.89), the Lévy distance (r=0.89) and the absolute standardized mean difference (r=0.90) were similar, whereas this was lower for the overlapping coefficient (r=-0.42). When sample size decreased to 400, mean based measures of balance had stronger correlations with bias. Models including all confounding variables, their squares and interaction terms resulted in smaller bias than models that included only main terms for confounding variables.

CONCLUSIONS: We conclude that measures for balance are useful for reporting the amount of balance reached in propensity score analysis and can be helpful in selecting the final PS model. Copyright © 2011 John Wiley & Sons, Ltd.

PMID: 21805529 [PubMed - as supplied by publisher]

2. Stat Med. 2011 Jul 29. doi: 10.1002/sim.4324. [Epub ahead of print]

Analyzing direct and indirect effects of treatment using dynamic path analysis applied to data from the Swiss HIV Cohort Study. Røysland K, Gran JM, Ledergerber B, Wyl V, Young J, Aalen OO. Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Norway. kjetil.roysland@medisin.uio.no.

When applying survival analysis, such as Cox regression, to data from major clinical trials or other studies, often only baseline covariates are used. This is typically the case even if updated covariates are available throughout the observation period, which leaves large amounts of information unused. The main reason for this is that such time-dependent covariates often are internal to the disease process, as they are influenced by treatment, and therefore lead to confounded estimates of the treatment effect. There are, however, methods to exploit such covariate information in a useful way. We study the method of dynamic path analysis applied to data from the Swiss HIV Cohort Study. To adjust for time-dependent confounding between treatment and the outcome ‘AIDS or death’, we carried out the analysis on a sequence of mimicked randomized trials constructed from the original cohort data. To analyze these trials together, regular dynamic path analysis is extended to a composite analysis of weighted dynamic path models. Results using a simple path model, with one indirect effect mediated through current HIV-1 RNA level, show that most or all of the total effect go through HIV-1 RNA for the first 4 years. A similar model, but with CD4 level as mediating variable, shows a weaker indirect effect, but the results are in the same direction. There are many reasons to be cautious when drawing conclusions from estimates of direct and indirect effects. Dynamic path analysis is however a useful tool to explore underlying processes, which are ignored in regular analyses. Copyright © 2011 John Wiley & Sons, Ltd.

PMID: 21800346 [PubMed - as supplied by publisher]

3. Arch Intern Med. 2011 Jul 25. [Epub ahead of print]

Predicting Death: An Empirical Evaluation of Predictive Tools for Mortality. Siontis GC, Tzoulaki I, Ioannidis JP.University of Ioannina School of Medicine, Ioannina, Greece (Drs Siontis, Tzoulaki, and Ioannidis); Department of Epidemiology and Biostatistics, Imperial College of Medicine, London, England (Drs Tzoulaki and Ioannidis); the Institute for Clinical Research and Health Policy Studies, Department of Medicine, Tufts University School of Medicine, Boston, Massachusetts (Dr Ioannidis); the Department of Epidemiology, Harvard School of Public Health, Boston (Dr Ioannidis); and the Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, California (Dr Ioannidis).

BACKGROUND: The ability to predict death is crucial in medicine, and many relevant prognostic tools have been developed for application in diverse settings. We aimed to evaluate the discriminating performance of predictive tools for death and the variability in this performance across different clinical conditions and studies.

METHODS: We used Medline to identify studies published in 2009 that assessed the accuracy (based on the area under the receiver operating characteristic curve [AUC]) of validated tools for predicting all-cause mortality. For tools where accuracy was reported in 4 or more assessments, we calculated summary accuracy measures. Characteristics of studies of the predictive tools were evaluated to determine if they were associated with the reported accuracy of the tool.

RESULTS: A total of 94 eligible studies provided data on 240 assessments of 118 predictive tools. The AUC ranged from 0.43 to 0.98 (median [interquartile range], 0.77 [0.71-0.83]), with only 23 of the assessments reporting excellent discrimination (10%) (AUC, >0.90). For 10 tools, accuracy was reported in 4 or more assessments; only 1 tool had a summary AUC exceeding 0.80. Established tools showed large heterogeneity in their performance across different cohorts (I(2) range, 68%-95%). Reported AUC was higher for tools published in journals with lower impact factor (P = .01), with larger sample size (P = .01), and for those that aimed to predict mortality among the highest-risk patients (P = .002) and among children (P < .001).

CONCLUSIONS: Most tools designed to predict mortality have only modest accuracy, and there is large variability across various diseases and populations. Most proposed tools do not have documented clinical utility.

PMID: 21788535 [PubMed - as supplied by publisher]

4. Am J Epidemiol. 2011 Jul 16. [Epub ahead of print]

Reducing the Variance of the Prescribing Preference-based Instrumental Variable Estimates of the Treatment Effect. Abrahamowicz M, Beauchamp ME, Ionescu-Ittu R, Delaney JA, Pilote L.

Instrumental variable (IV) methods based on the physician’s prescribing preference may remove bias due to unobserved confounding in pharmacoepidemiologic studies. However, IV estimates, originally defined as the treatment prescribed for a single previous patient of a given physician, show important variance inflation. The authors proposed and validated in simulations a new method to reduce the variance of IV estimates even when physicians’ preferences change over time. First, a potential “change-time,” after which the physician’s preference has changed, was estimated for each physician. Next, all patients of a given physician were divided into 2 homogeneous subsets: those treated before the change-time versus those treated after the change-time. The new IV was defined as the proportion of all previous patients in a corresponding homogeneous subset who were prescribed a specific drug. In simulations, all alternative IV estimators avoided strong bias of the conventional estimates. The change-time method reduced the standard deviation of the estimates by approximately 30% relative to the original previous patient-based IV. In an empirical example, the proposed IV correlated better with the actual treatment and yielded smaller standard errors than alternative IV estimators. Therefore, the new method improved the overall accuracy of IV estimates in studies with unobserved confounding and time-varying prescribing preferences.

PMID: 21765169 [PubMed - as supplied by publisher]

5. Am J Epidemiol. 2011 Jul 12. [Epub ahead of print]

Performance of Disease Risk Scores, Propensity Scores, and Traditional Multivariable Outcome Regression in the Presence of Multiple Confounders. Arbogast PG, Ray WA.

Propensity scores are widely used in cohort studies to improve performance of regression models when considering large numbers of covariates. Another type of summary score, the disease risk score (DRS), which estimates disease probability conditional on nonexposure, has also been suggested. However, little is known about how it compares with propensity scores. Monte Carlo simulations were conducted comparing regression models using the DRS and the propensity score with models that directly adjust for all of the individual covariates. The DRS was calculated in 2 ways: from the unexposed population and from the full cohort. Compared with traditional multivariable outcome regression models, all 3 summary scores had comparable performance for moderate correlation between exposure and covariates and, for strong correlation, the full-cohort DRS and propensity score had comparable performance. When traditional methods had model misspecification, propensity scores and the full-cohort DRS had superior performance. All 4 models were affected by the number of events per covariate, with propensity scores and traditional multivariable outcome regression least affected. These data suggest that, for cohort studies for which covariates are not highly correlated with exposure, the DRS, particularly that calculated from the full cohort, is a useful tool.

PMID: 21749976 [PubMed - as supplied by publisher]

6. Epidemiology. 2011 Jul 8. [Epub ahead of print]

A Comparison of Methods to Estimate the Hazard Ratio Under Conditions of Time-varying Confounding and Nonpositivity. Naimi AI, Cole SR, Westreich DJ, Richardson DB. Department of Epidemiology, Gillings School of Global Public Health, UNC-Chapel Hill, NC and Department of Obstetrics and Gynecology and Duke Global Health Institute, Duke University.

In occupational epidemiologic studies, the healthy worker survivor effect refers to a process that leads to bias in the estimates of an association between cumulative exposure and a health outcome. In these settings, work status acts both as an intermediate and confounding variable and may violate the positivity assumption (the presence of exposed and unexposed observations in all strata of the confounder). Using Monte Carlo simulation, we assessed the degree to which crude, work-status adjusted, and weighted (marginal structural) Cox proportional hazards models are biased in the presence of time-varying confounding and nonpositivity. We simulated the data representing time-varying occupational exposure, work status, and mortality. Bias, coverage, and root mean squared error (MSE) were calculated relative to the true marginal exposure effect in a range of scenarios. For a base-case scenario, using crude, adjusted, and weighted Cox models, respectively, the hazard ratio was biased downward 19%, 9%, and 6%; 95% confidence interval coverage was 48%, 85%, and 91%; and root MSE was 0.20, 0.13, and 0.11. Although marginal structural models were less biased in most scenarios studied, neither standard nor marginal structural Cox proportional hazards models fully resolve the bias encountered under conditions of time-varying confounding and nonpositivity.

PMID: 21747286 [PubMed - as supplied by publisher]

CER Scan [published within the last 30 days]

1. BMC Health Serv Res. 2011 Jul 21;11(1):171. [Epub ahead of print]

Does adding risk-trends to survival models improve in-hospital mortality predictions? A cohort study. Wong J, Taljaard M, Forster AJ, van Walraven C.

BACKGROUND: Clinicians informally assess changes in patients’ status over time to prognosticate their outcomes. The incorporation of trends in patient status into regression models could improve their ability to predict outcomes. In this study, we used a unique approach to measure trends in patient hospital death risk and determined whether the incorporation of these trend measures into a survival model improved the accuracy of its risk predictions.

METHODS: We included all adult inpatient hospitalizations between 1 April 2004 and 31 March 2009 at our institution. We used the daily mortality risk scores from an existing time-dependent survival model to create five trend indicators: absolute and relative percent change in the risk score from the previous day; absolute and relative percent change in the risk score from the start of the trend; and number of days with a trend in the risk score. In the derivation set, we determined which trend indicators were associated with time to death in hospital, independent of the existing covariates. In the validation set, we compared the predictive performance of the existing model with and without the trend indicators.

RESULTS: Three trend indicators were independently associated with time to hospital mortality: the absolute change in the risk score from the previous day; the absolute change in the risk score from the start of the trend; and the number of consecutive days with a trend in the risk score. However, adding these trend indicators to the existing model resulted in only small improvements in model discrimination and calibration.

CONCLUSIONS: We produced several indicators of trend in patient risk that were significantly associated with time to hospital death independent of the model used to create them. In other survival models, our approach of incorporating risk trends could be explored to improve their performance without the collection of additional data.

PMID: 21777460 [PubMed - as supplied by publisher]

Open Access: http://www.biomedcentral.com/content/pdf/1472-6963-11-171.pdf

2. Stat Med. 2011 Jul 20;30(16):1917-32. doi: 10.1002/sim.4262. Epub 2011 May 3.

Alternative methods for testing treatment effects on the basis of multiple outcomes: Simulation and case study. Yoon FB, Fitzmaurice GM, Lipsitz SR, Horton NJ, Laird NM, Normand SL. Harvard Medical School, Boston, MA, U.S.A.. yoon@hcp.med.harvard.edu.

In clinical trials multiple outcomes are often used to assess treatment interventions. This paper presents an evaluation of likelihood-based methods for jointly testing treatment effects in clinical trials with multiple continuous outcomes. Specifically, we compare the power of joint tests of treatment effects obtained from joint models for the multiple outcomes with univariate tests based on modeling the outcomes separately. We also consider the power and bias of tests when data are missing, a common feature of many trials, especially in psychiatry. Our results suggest that joint tests capitalize on the correlation of multiple outcomes and are more powerful than standard univariate methods, especially when outcomes are missing completely at random. When outcomes are missing at random, test procedures based on correctly specified joint models are unbiased, while standard univariate procedures are not. Results of a simulation study are reported, and the methods are illustrated in an example from the Clinical Antipsychotic Trials of Intervention Effectiveness for schizophrenia. Copyright © 2011 John Wiley & Sons, Ltd.

PMCID: PMC3116112 [Available on 2012/7/20]

PMID: 21538986 [PubMed - in process]

3. Health Serv Res. 2011 Aug;46(4):1259-80. doi: 10.1111/j.1475-6773.2011.01253.x. Epub 2011 Mar 17.

Crowd-out and Exposure Effects of Physical Comorbidities on Mental Health Care Use: Implications for Racial-Ethnic Disparities in Access. Lê Cook B, McGuire TG, Alegría M, Normand SL. Center for Multicultural Mental Health Research, 120 Beacon St., 4th Floor, Somerville, MA 02143 Department of Psychiatry, Harvard Medical School, Boston, MA Department of Health Care Policy, Harvard Medical School, Boston, MA Center for Multicultural Mental Health Research, Somerville, MA.

Objectives. In disparities models, researchers adjust for differences in “clinical need,” including indicators of comorbidities. We reconsider this practice, assessing (1) if and how having a comorbidity changes the likelihood of recognition and treatment of mental illness; and (2) differences in mental health care disparities estimates with and without adjustment for comorbidities. Data. Longitudinal data from 2000 to 2007 Medical Expenditure Panel Survey (n=11,083) split into pre and postperiods for white, Latino, and black adults with probable need for mental health care. Study Design. First, we tested a crowd-out effect (comorbidities decrease initiation of mental health care after a primary care provider [PCP] visit) using logistic regression models and an exposure effect (comorbidities cause more PCP visits, increasing initiation of mental health care) using instrumental variable methods. Second, we assessed the impact of adjustment for comorbidities on disparity estimates. Principal Findings. We found no evidence of a crowd-out effect but strong evidence for an exposure effect. Number of postperiod visits positively predicted initiation of mental health care. Adjusting for racial/ethnic differences in comorbidities increased black-white disparities and decreased Latino-white disparities. Conclusions. Positive exposure findings suggest that intensive follow-up programs shown to reduce disparities in chronic-care management may have additional indirect effects on reducing mental health care disparities.

PMCID: PMC3130831 [Available on 2012/8/1]

PMID: 21413984 [PubMed - in process]

Theme: CER Education

1. Pharmacoepidemiol Drug Saf. 2011 Aug;20(8):797-804. doi: 10.1002/pds.2100. Epub 2011 Jan 10.

Curricular considerations for pharmaceutical comparative effectiveness research. Murray MD. Purdue University College of Pharmacy and Regenstrief Institute, Indianapolis, USA. mmurray@regenstrief.org.

In the U.S. pharmacoepidemiology and related health professions can potentially flourish with the congressional appropriation of $1.1 billion of federal funding for comparative effectiveness research (CER). A direct result of this legislation will be the need for sufficient numbers of trained scientists and decision-makers to address the research and implementation associated with CER. An interdisciplinary expert panel comprised mostly of professionals with pharmaceutical interests was convened to examine the knowledge, skills, and abilities to be considered in the development of a CER curriculum for the health professions focusing predominantly on pharmaceuticals. A limitation of the panel’s composition was that it did not represent the breadth of comparative effectiveness research, which additionally includes devices, services, diagnostics, behavioral treatments, and delivery system changes. This bias affects the generalizability of these findings. Notwithstanding, important components of the curriculum identified by the panel included study design considerations and understanding the strengths and limitations of data sources. Important skills and abilities included methods for adjustment of differences in comparator group characteristics to control confounding and bias, data management skills, and clinical skills and insights into the relevance of comparisons. Most of the knowledge, skills, and abilities identified by the panel were consistent with the training of pharmacoepidemiologists. While comparative effectiveness is broader than the pharmaceutical sciences, pharmacoepidemiologists have much to offer academic and professional CER training programs. As such, pharmacoepidemiologists should have a central role in curricular design and provision of the necessary training for needed comparative effectiveness researchers within the realm of pharmaceutical sciences. Copyright © 2011 John Wiley & Sons, Ltd.

PMID: 21796716 [PubMed - in process]

2. Pharmacoepidemiol Drug Saf. 2011 Aug;20(8):805-6. doi: 10.1002/pds.2122. Epub 2011 May 25.

The central role of pharmacoepidemiology in comparative effectiveness research education: critical next steps. Selker HP. Tufts Clinical and Translational Science Institute, Tufts University, Boston, MA, USA; Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA. hselker@tuftsmedicalcenter.org.

PMID: 21618339 [PubMed - in process]

3. Pharmacoepidemiol Drug Saf. 2011 Aug;20(8):807-9. doi: 10.1002/pds.2173. Epub 2011 Jun 17.

Starting the conversation. Lawrence W. Center for Outcomes and Evidence, Agency for Healthcare Research and Quality, 540 Gaither Rd., Rockville, MD, 20850, USA. William.lawrence@ahrq.hhs.gov.

PMID: 21681851 [PubMed - in process]

July 2011

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CER Scan [Published within the past 30 days]

1. Pharmacoepidemiol Drug Saf. 2011 Jun 30. doi: 10.1002/pds.2152. [Epub ahead of print]

Confounding adjustment via a semi-automated high-dimensional propensity score algorithm: an application to electronic medical records. Toh S, García Rodríguez LA, Hernán MA.Department of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, MA, USA. darrentoh@post.harvard.edu.

PURPOSE: A semi-automated high-dimensional propensity score (hd-PS) algorithm has been proposed to adjust for confounding in claims databases. The feasibility of using this algorithm in other types of healthcare databases is unknown.

METHODS: We estimated the comparative safety of traditional non-steroidal anti-inflammatory drugs (NSAIDs) and selective COX-2 inhibitors regarding the risk of upper gastrointestinal bleeding (UGIB) in The Health Improvement Network, an electronic medical record (EMR) database in the UK. We compared the adjusted effect estimates when the confounders were identified using expert knowledge or the semi-automated hd-PS algorithm.

RESULTS: Compared with the 411,616 traditional NSAID initiators, the crude odds ratio (OR) of UGIB was 1.50 (95%CI: 0.98, 2.28) for the 43,569 selective COX-2 inhibitor initiators. The OR dropped to 0.81 (0.52, 1.27) upon adjustment for known risk factors for UGIB that are typically available in both claims and EMR databases. The OR remained similar when further adjusting for covariates-smoking, alcohol consumption, and body mass index-that are not typically recorded in claims databases (OR 0.81; 0.51, 1.26) or adding 500 empirically identified covariates using the hd-PS algorithm (OR 0.78; 0.49, 1.22). Adjusting for age and sex plus 500 empirically identified covariates produced an OR of 0.87 (0.56, 1.34).

CONCLUSIONS: The hd-PS algorithm can be implemented in pharmacoepidemiologic studies that use primary care EMR databases such as The Health Improvement Network. For the NSAID-UGIB association for which major confounders are well known, further adjustment for covariates selected by the algorithm had little impact on the effect estimate. Copyright © 2011 John Wiley & Sons, Ltd.

PMID: 21717528 [PubMed - as supplied by publisher]

CER Scan [published within the last 2 months]

2. BMC Med Res Methodol. 2011 May 23;11:77.

Logistic random effects regression models: a comparison of statistical packages for binary and ordinal outcomes. Li B, Lingsma HF, Steyerberg EW, Lesaffre E.Department of Biostatistics, Erasmus MC, Dr, Molewaterplein 50, Rotterdam, the Netherlands. e.lesaffre@erasmusmc.nl.

BACKGROUND: Logistic random effects models are a popular tool to analyze multilevel also called hierarchical data with a binary or ordinal outcome. Here, we aim to compare different statistical software implementations of these models.

METHODS: We used individual patient data from 8509 patients in 231 centers with moderate and severe Traumatic Brain Injury (TBI) enrolled in eight Randomized Controlled Trials (RCTs) and three observational studies. We fitted logistic random effects regression models with the 5-point Glasgow Outcome Scale (GOS) as outcome, both dichotomized as well as ordinal, with center and/or trial as random effects, and as covariates age, motor score, pupil reactivity or trial. We then compared the implementations of frequentist and Bayesian methods to estimate the fixed and random effects. Frequentist approaches included R (lme4), Stata (GLLAMM), SAS (GLIMMIX and NLMIXED), MLwiN ([R]IGLS) and MIXOR, Bayesianapproaches included WinBUGS, MLwiN (MCMC), R package MCMCglmm and SAS experimental procedure MCMC.Three data sets (the full data set and two sub-datasets) were analysed using basically two logistic random effects models with either one random effect for the center or two random effects for center and trial. For the ordinal outcome in the full data set also a proportional odds model with a random center effect was fitted.

RESULTS: The packages gave similar parameter estimates for both the fixed and random effects and for the binary (and ordinal) models for the main study and when based on a relatively large number of level-1 (patient level) data compared to the number of level-2 (hospital level) data. However, when based on relatively sparse data set, i.e. when the numbers of level-1 and level-2 data units were about the same, the frequentist and Bayesian approaches showed somewhat different results. The software implementations differ considerably in flexibility, computation time, and usability. There are also differences in the availability of additional tools for model evaluation, such as diagnostic plots. The experimental SAS (version 9.2) procedure MCMC appeared to be inefficient.

CONCLUSIONS: On relatively large data sets, the different software implementations of logistic random effects regression models produced similar results. Thus, for a large data set there seems to be no explicit preference (of course if there is no preference from a philosophical point of view) for either afrequentist or Bayesian approach (if based on vague priors). The choice for a particular implementation may largely depend on the desired flexibility, and the usability of the package. For small data sets the random effects variances are difficult to estimate. In the frequentist approaches the MLE of this variance was often estimated zero with a standard error that is either zero or could not be determined, while for Bayesian methods the estimates could depend on the chosen”non-informative” prior of the variance parameter. The starting value for the variance parameter may be also critical for the convergence of the Markov chain.

PMCID: PMC3112198 PMID: 21605357 [PubMed - in process]

Free article: http://www.ncbi.nlm.nih.gov/pnc/articles/PMC3112198/?tool=pubmed

We recommend reviewing Supplemental Material: Additional File #2

3. Stat Med. 2011 Jul 10;30(15):1837-51. doi: 10.1002/sim.4240. Epub 2011 Apr 15.

Semiparametric regression models for detecting effect modification in matched case-crossover studies. Kim I, Cheong HK, Kim H. Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA, U.S.A.

In matched case-crossover studies, it is generally accepted that covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression model because any stratum effect is removed by the conditioning on the fixed number of sets of a case and controls in the stratum. Hence, the conditional logistic regression model is not able to detect any effects associated with the matching covariates by stratum. In addition, the matching covariates may be effect modification and the methods for assessing and characterizing effect modification by matching covariates are quite limited. In this article, we propose a unified approach in its ability to detect both parametric and nonparametric relationships between the predictor and the relative risk of disease or binary outcome, as well as potential effect modifications by matching covariates. Two methods are developed using two semiparametric models: (1) the regression spline varying coefficients model and (2) the regression spline interaction model. Simulation results show that the two approaches are comparable. These methods can be used in any matched case-control study and extend to multilevel effect modification studies. We demonstrate the advantage of our approach using an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis associated with drinking water turbidity. Copyright © 2011 John Wiley & Sons, Ltd.

PMID: 21495061 [PubMed - in process]

Theme: Data Linkage

1. J Clin Epidemiol. 2011 May;64(5):565-72. Epub 2010 Oct 16.

Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage. Tromp M, Ravelli AC, Bonsel GJ, Hasman A, Reitsma JB. Department of Medical Informatics, Academic Medical Center, University of Amsterdam, 1100 DE Amsterdam, The Netherlands. m.tromp@amc.uva.nl

OBJECTIVE: To gain insight into the performance of deterministic record linkage (DRL) vs. probabilistic record linkage (PRL) strategies under different conditions by varying the frequency of registration errors and the amount of discriminating power.

STUDY DESIGN AND SETTING: A simulation study in which data characteristics were varied to create a range of realistic linkage scenarios. For each scenario, we compared the number of misclassifications (number of false nonlinks and false links) made by the different linking strategies: deterministic full, deterministic N-1, and probabilistic.

RESULTS: The full deterministic strategy produced the lowest number of false positive links but at the expense of missing considerable numbers of matches dependent on the error rate of the linking variables. The probabilistic strategy outperformed the deterministic strategy (full or N-1) across all scenarios. A deterministic strategy can match the performance of a probabilistic approach providing that the decision about which disagreements should be tolerated is made correctly. This requires a priori knowledge about the quality of all linking variables, whereas this information is inherently generated by a probabilistic strategy.

CONCLUSION: PRL is more flexible and provides data about the quality of the linkage process that in turn can minimize the degree of linking errors, given the data provided.

PMID: 20952162 [PubMed - indexed for MEDLINE]

2. Am J Epidemiol. 2011 May 1;173(9):1059-68. Epub 2011 Mar 23.

Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester epidemiology project. St Sauver JL, Grossardt BR, Yawn BP, Melton LJ 3rd, Rocca WA. Division of Epidemiology, Department of Health Sciences Research, College of Medicine, Mayo Clinic, Rochester, Minnesota 55905, USA.

The Rochester Epidemiology Project (REP) is a unique research infrastructure in which the medical records of virtually all persons residing in Olmsted County, Minnesota, for over 40 years have been linked and archived. In the present article, the authors describe how the REP links medical records from multiple health care institutions to specific individuals and how residency is confirmed over time. Additionally, the authors provide evidence for the validity of the REP Census enumeration. Between 1966 and 2008, 1,145,856 medical records were linked to 486,564 individuals in the REP. The REP Census was found to be valid when compared with a list of residents obtained from random digit dialing, a list of residents of nursing homes and senior citizen complexes, a commercial list of residents, and a manual review of records. In addition, the REP Census counts were comparable to those of 4 decennial US censuses (e.g., it included 104.1% of 1970 and 102.7% of 2000 census counts). The duration for which each person was captured in the system varied greatly by age and calendar year; however, the duration was typically substantial. Comprehensive medical records linkage systems like the REP can be used to maintain a continuously updated census and to provide an optimal sampling framework for epidemiologic studies.

PMCID: PMC3105274 [Available on 2012/5/1] PMID: 21430193 [PubMed - indexed for MEDLINE]

3. Stat Methods Med Res. 2011 Jun 10. [Epub ahead of print]

Linkage of patient records from disparate sources. Li X, Shen C. Division of Biostatistics, Indiana University School of Medicine, Indianapolis, US.

We review ideas, approaches and progress in the field of record linkage. We point out that the latent class models used in probabilistic matching have been well developed and applied in a different context of diagnostic testing when the true disease status is unknown. The methodology developed in the diagnostic testing setting can be potentially translated and applied in record linkage. Although there are many methods for record linkage, a comprehensive evaluation of methods for a wide range of real-world data with different data characteristics and with true match status is absent due to lack of data sharing. However, the recent availability of generators of synthetic data with realistic characteristics renders such evaluations feasible.

PMID: 21665896 [PubMed - as supplied by publisher]

June 2011

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CER Scan [Published within the past 30 days]

1. Clin Trials. 2011 May 24. [Epub ahead of print]

Confounding due to changing background risk in adaptively randomized trials. Lipsky AM, Greenland S. Gertner Institute for Epidemiology and Health Policy Research, Chaim Sheba Medical Center, Tel Hashomer, Israel.

BACKGROUND: While adaptive trials tend to improve efficiency, they are also subject to some unique biases. PURPOSE: We address a bias that arises from adaptive randomization in the setting of a time trend in disease incidence.

METHODS: We use a potential-outcome model and directed acyclic graphs to illustrate the bias that arises from a changing subject allocation ratio with a concurrent change in background risk.

RESULTS: In a trial that uses adaptive randomization, time trends in risk can bias the crude effect estimate obtained by naively combining the data from the different stages of the trial. We illustrate how the bias arises from an interplay of departures from exchangeability among groups and the changing randomization proportions.

LIMITATIONS: We focus on risk-ratio and risk-difference analysis.

CONCLUSIONS: Analysis of trials using adaptive randomization should involve attention to or adjustment for possible trends in background risk. Numerous modeling strategies are available for that purpose, including stratification, trend modeling, inverse-probability-of-treatment weighting, and hierarchical regression.

PMID: 21610005 [PubMed - as supplied by publisher]

2. Pharmacoepidemiol Drug Saf. 2011 May 30. doi: 10.1002/pds.2121. [Epub ahead of print]

Simultaneously assessing intended and unintended treatment effects of multiple treatment options: a pragmatic “matrix design.” Rassen JA, Solomon DH, Glynn RJ, Schneeweiss S. Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA. jrassen@post.harvard.edu.

PURPOSE: A key aspect of comparative effectiveness research is the assessment of competing treatment options and multiple outcomes rather than a single treatment option and a single benefit or harm. In this commentary, we describe a methodological framework that supports the simultaneous examination of a “matrix” of treatments and outcomes in non-randomized data.

METHODS: We outline the methodological challenges to a matrix-type study (matrix design). We consider propensity score matching with multiple treatment groups, statistical analysis, and choice of association measure when evaluating multiple outcomes. We also discuss multiple testing, use of high-dimensional propensity scores for covariate balancing in light of multiple outcomes, and suitability of available software.

CONCLUSION: The matrix design study methods facilitate examination of the comparative benefits and harms of competing treatment choices, and also provides the input required for calculating the numbers needed to treat and for a broader benefit/harm assessment that weighs endpoints of varying severity. Copyright © 2011 John Wiley & Sons, Ltd.

PMID: 21626604 [PubMed - as supplied by publisher]

3. Stat Med. 2011 May 20;30(11):1199-217. doi: 10.1002/sim.4156. Epub 2010 Dec 29.

Exploring the benefits of adaptive sequential designs in time-to-event endpoint settings. Emerson SC, Rudser KD, Emerson SS. Department of Statistics, Oregon State University, U.S.A.. scemerson@gmail.com.

Sequential analysis is frequently employed to address ethical and financial issues in clinical trials. Sequential analysis may be performed using standard group sequential designs, or, more recently, with adaptive designs that use estimates of treatment effect to modify the maximal statistical information to be collected. In the general setting in which statistical information and clinical trial costs are functions of the number of subjects used, it has yet to be established whether there is any major efficiency advantage to adaptive designs over traditional group sequential designs. In survival analysis, however, statistical information (and hence efficiency) is most closely related to the observed number of events, while trial costs still depend on the number of patients accrued. As the number of subjects may dominate the cost of a trial, an adaptive design that specifies a reduced maximal possible sample size when an extreme treatment effect has been observed may allow early termination of accrual and therefore a more cost-efficient trial. We investigate and compare the tradeoffs between efficiency (as measured by average number of observed events required), power, and cost (a function of the number of subjects accrued and length of observation) for standard group sequential methods and an adaptive design that allows for early termination of accrual. We find that when certain trial design parameters are constrained, an adaptive approach to terminating subject accrual may improve upon the cost efficiency of a group sequential clinical trial investigating time-to-event endpoints. However, when the spectrum of group sequential designs considered is broadened, the advantage of the adaptive designs is less clear. Copyright © 2010 John Wiley & Sons, Ltd.

PMID: 21538450 [PubMed - in process]

4. Stat Methods Med Res. 2011 Jun;20(3):191-215. Epub 2008 Nov 26.

Estimating dose-response effects in psychological treatment trials: the role of instrumental variables. Maracy M, Dunn G. Biostatistics, Health Methodology Research Group, School of Community Based Medicine, University of Manchester, UK.

We present a relatively non-technical and practically orientated review of statistical methods that can be used to estimate dose-response relationships in randomised controlled psychotherapy trials in which participants fail to attend all of the planned sessions of therapy. Here we are investigating the effects on treatment outcome of the number of sessions attended when the latter is possibly subject to hidden selection effects (hidden confounding). The aim is to estimate the parameters of a structural mean model (SMM) using randomisation, and possibly randomisation by covariate interactions, as instrumental variables. We describe, compare and illustrate the equivalence of the use of a simple G-estimation algorithm and two two-stage least squares procedures that are traditionally used in economics.

PMID: 19036909 [PubMed - in process]

May 2011

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CER Scan – Published within the past 30 days

1. BMC Med Res Methodol. 2011 Apr 25;11(1):57. [Epub ahead of print]

Exploratory trials, confirmatory observations: a new reasoning model in the era of patient-centered medicine. Sacristan JA.
BACKGROUND:
The prevailing view in therapeutic clinical research today is that observational studies are useful for generating new hypotheses and that controlled experiments (i.e., randomized clinical trials, RCTs) are the most appropriate method for assessing and confirming the efficacy of interventions.

DISCUSSION:
The current trend towards patient-centered medicine calls for alternative ways of reasoning, and in particular for a shift towards hypothetico-deductive logic, in which theory is adjusted in light of individual facts. A new model of this kind should change our approach to drug research and development, and regulation. The assessment of new therapeutic agents would be viewed as a continuous process, and regulatory approval would no longer be regarded as the final step in the testing of a hypothesis, but rather, as the hypothesis-generating step. The main role of RCTs in this patient-centered research paradigm would be to generate hypotheses, while observations would serve primarily to test their validity for different types of patients. Under hypothetico-deductive logic, RCTs are considered “exploratory” and observations, “confirmatory”.

SUMMARY:
In this era of tailored therapeutics, the answers to therapeutic questions cannot come exclusively from methods that rely on data aggregation, the analysis of similarities, controlled experiments, and a search for the best outcome for the average patient; they must also come from methods based on data disaggregation, analysis of subgroups and individuals, an integration of research and clinical practice, systematic observations, and a search for the best outcome for the individual patient. We must look not only to evidence-based medicine, but also to medicine-based evidence, in seeking the knowledge that we need.

Free Article: http://www.biomedcentral.com/1471-2288/11/57

PMID: 21518440

2. JAMA. 2011 Apr 13;305(14):1482-3.

Infection prevention and comparative effectiveness research. Perencevich EN, Lautenbach E.

Division of General Internal Medicine, University of Iowa, Carver College of Medicine, Iowa City, USA. eli-perencevich@uiowa.edu

PMID: 21486981

CER Scan [Epub Ahead of Print]

1. Stat Med. 2011 May 3. doi: 10.1002/sim.4262. [Epub ahead of print]

Alternative methods for testing treatment effects on the basis of multiple outcomes: Simulation and case study. Yoon FB, Fitzmaurice GM, Lipsitz SR, Horton NJ, Laird NM, Normand SL. Harvard Medical School, Boston, MA, U.S.A.. yoon@hcp.med.harvard.edu.
In clinical trials multiple outcomes are often used to assess treatment interventions. This paper presents an evaluation of likelihood-based methods for jointly testing treatment effects in clinical trials with multiple continuous outcomes. Specifically, we compare the power of joint tests of treatment effects obtained from joint models for the multiple outcomes with univariate tests based on modeling the outcomes separately. We also consider the power and bias of tests when data are missing, a common feature of many trials, especially in psychiatry. Our results suggest that joint tests capitalize on the correlation of multiple outcomes and are more powerful than standard univariate methods, especially when outcomes are missing completely at random. When outcomes are missing at random, test procedures based on correctly specified joint models are unbiased, while standard univariate procedures are not. Results of a simulation study are reported, and the methods are illustrated in an example from the Clinical
Antipsychotic Trials of Intervention Effectiveness for schizophrenia.

Copyright ©2011 John Wiley & Sons, Ltd.

PMID: 21538986 [PubMed - as supplied by publisher]

2. Stat Med. 2011 Apr 15. doi: 10.1002/sim.4241. [Epub ahead of print]

Two-stage instrumental variable methods for estimating the causal o dds ratio: Analysis of bias. Cai B, Small DS, TenHave TR.

Merck Research Laboratories, UG1D-60, P.O. Box 1000, North Wales, PA 19454-1099, U.S.A.; Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Blockley Hall, 6th FLR 423 Guardian Dr., Philadelphia, PA 19104-6021, U.S.A.. bcai@mail.med.upenn.edu.
We present closed-form expressions of asymptotic bias for the causal odds ratio from two estimation approaches of instrumental variable logistic regression: (i) the two-stage predictor substitution (2SPS) method and (ii) the two-stage residual inclusion (2SRI) approach. Under the 2SPS approach, the first stage model yields the predicted value of treatment as a function of an instrument and covariates, and in the second stage model for the outcome, this predicted value replaces the observed value of treatment as a covariate. Under the 2SRI approach, the first stage is the same, but the residual term of the first stage regression is included in the second stage regression, retaining the observed treatment as a covariate. Our bias assessment is for a different context from that of Terza (J. Health Econ. 2008; 27(3):531-543), who focused on the causal odds ratio conditional on the unmeasured confounder, whereas we focus on the causal odds ratio among compliers under the principal stratification framework. Our closed-form bias results show that the 2SPS logistic regression generates asymptotically biased estimates of this causal odds ratio when there is no unmeasured confounding and that this bias increases with increasing unmeasured confounding. The 2SRI logistic regression is asymptotically unbiased when there is no unmeasured confounding, but when there is unmeasured confounding, there is bias and it increases with increasing unmeasured confounding. The closed-form bias results provide guidance for using these IV logistic regression methods. Our simulation results are consistent with our closed-form analytic results under different combinations of parameter settings. Copyright © 2011 John Wiley & Sons, Ltd.

Copyright © 2011 John Wiley & Sons, Ltd.

PMID: 21495062

3. Stat Med. 2011 Apr 15. doi: 10.1002/sim.4240. [Epub ahead of print]

Semiparametric regression models for detecting effect mo dification in matched case-crossover studies. Kim I, Cheong HK, Kim H.

Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA, U.S.A.
In matched case-crossover studies, it is generally accepted that covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression model because any stratum effect is removed by the conditioning on the fixed number of sets of a case and controls in the stratum. Hence, the conditional logistic regression model is not able to detect any effects associated with the matching covariates by stratum. In addition, the matching covariates may be effect modification and the methods for assessing and characterizing effect modification by matching covariates are quite limited. In this article, we propose a unified approach in its ability to detect both parametric and nonparametric relationships between the predictor and the relative risk of disease or binary outcome, as well as potential effect modifications by matching covariates. Two methods are developed using two semiparametric models: (1) the regression spline varying coefficients model and (2) the regression spline interaction model. Simulation results show that the two approaches are comparable. These methods can be used in any matched case-control study and extend to multilevel effect modification studies. We demonstrate the advantage of our approach using an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis associated with drinking water turbidity. Copyright © 2011 John Wiley & Sons, Ltd.

PMID: 21495061

4. Pharmacoepidemiol Drug Saf. 2011 Apr 29. doi: 10.1002/pds.2133. [Epub ahead of print]

Near real-time vaccine safety surveillance with partially accrued data. Greene SK, Kulldorff M, Yin R, Yih WK, Lieu TA, Weintraub ES, Lee GM.

Department of Population Medicine, Harvard Medical School and Harvard Pilgrim
Health Care Institute, Boston, MA, USA. Sharon_Greene@harvardpilgrim.org.
PURPOSE: The Vaccine Safety Datalink (VSD) Project conducts near real-time vaccine safety surveillance using sequential analytic methods. Timely surveillance is critical in identifying potential safety problems and preventing additional exposure before most vaccines are administered. For vaccines that are administered during a short period, such as influenza vaccines, timeliness can be improved by undertaking analyses while risk windows following vaccination are ongoing and by accommodating predictable and unpredictable data accrual delays. We describe practical solutions to these challenges, which were adopted by the
VSD Project during pandemic and seasonal influenza vaccine safety surveillance in 2009/2010.

METHODS: Adjustments were made to two sequential analytic approaches.
The Poisson-based approach compared the number of pre-defined adverse events observed following vaccination with the number expected using historical data.
The expected number was adjusted for the proportion of the risk window elapsed and the proportion of inpatient data estimated to have accrued. The binomial-based approach used a self-controlled design, comparing the observed numbers of events in risk versus comparison windows. Events were included in analysis only if they occurred during a week that had already passed for both windows. RESULTS: Analyzing data before risk windows fully elapsed improved the timeliness of safety surveillance. Adjustments for data accrual lags were tailored to each data source and avoided biasing analyses away from detecting a potential safety problem, particularly early during surveillance.

CONCLUSIONS: The timeliness of vaccine and drug safety surveillance can be improved by properly accounting for partially elapsed windows and data accrual delays. Copyright © 2011 John Wiley & Sons, Ltd.

PMID: 21538670 [PubMed - as supplied by publisher]

5. Epidemiology. 2011 Apr 11. [Epub ahead of print]

Validation Data-based Adjustments for Outcome Misclassification in Logistic Regression: An Illustration. Lyles RH, Tang L, Superak HM, King CC, Celentano DD, Lo Y, Sobel JD.
From the Department of Biostatistics and Bioinformatics, The Rollins School of Public Health of Emory University, Atlanta, GA; Centers for Disease Control and Prevention, Atlanta, GA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY; and Department of Medicine, Wayne State University School of Medicine, Detroit, MI.
Misclassification of binary outcome variables is a known source of potentially serious bias when estimating adjusted odds ratios. Although researchers have described frequentist and Bayesian methods for dealing with the problem, these methods have seldom fully bridged the gap between statistical research and epidemiologic practice. In particular, there have been few real-world applications of readily grasped and computationally accessible methods that make direct use of internal validation data to adjust for differential outcome misclassification in logistic regression. In this paper, we illustrate likelihood-based methods for this purpose that can be implemented using standard statistical software. Using main study and internal validation data from the HIV Epidemiology Research Study, we demonstrate how misclassification rates can depend on the values of subject-specific covariates, and we illustrate the importance of accounting for this dependence. Simulation studies confirm the effectiveness of the maximum likelihood approach. We emphasize clear exposition of the likelihood function itself, to permit the reader to easily assimilate appended computer code that facilitates sensitivity analyses as well as the efficient handling of main/external and main/internal validation-study data. These methods are readily applicable under random cross-sectional sampling, and we discuss the extent to which the main/internal analysis remains appropriate under outcome-dependent (case-control) sampling.

PMID: 21487295 [PubMed - as supplied by publisher]

Theme: The Changing Face of Epidemiology

1. Epidemiology. 2011 May;22(3):295-7.

Making observational studies count: shaping the future of comparative effectiveness research. Dreyer NA. Outcome Sciences Inc., Cambridge, MA.

PMID: 21464648

2. Epidemiology. 2011 May;22(3):290-1.

With great data comes great responsibility: publishing comparative effectiveness research in epidemiology. Hernán MA. From Harvard School of Public Heath, Boston, MA.

PMID: 21464646 [PubMed - in process]

3. Epidemiology. 2011 May;22(3):302-4.

Improving automated database studies. Ray WA. From the The Division of Pharmacoepidemiology, Department of Preventive Medicine, Vanderbilt University School of Medicine, Nashville, TN; and bGeriatric Research, Education and Clinical Center, Nashville Veterans Administration Medical Center, Nashville, TN.

PMID: 21464650 [PubMed - in process]

4. Epidemiology. 2011 May;22(3):298-301.

Nonexperimental comparative effectiveness research using linked healthcare databases. Stürmer T, Jonsson Funk M, Poole C, Brookhart MA. From the Pharmacoepidemiology Program, Department of Epidemiology, UNC Gillings School of Global Public Health University of North Carolina at Chapel Hill, Chapel Hill, NC.

PMID: 21464649 [PubMed - in process]

5. Epidemiology. 2011 May;22(3):292-4.

The new world of data linkages in clinical epidemiology: are we being brave or foolhardy? Weiss NS. From the Department of Epidemiology, University of Washington, and the Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA.

PMID: 21464647 [PubMed - in process]

April 2011

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CER Scan – Published within the past 30 days

1. BMC Med Res Methodol. 2011 Apr 1;11(1):36. [Epub ahead of print]

Design of cohort studies in chronic diseases using routinely collected databases when a prescription is used as surrogate outcome.

Lodi S, Carpenter J, Egger P, Evans S.

BACKGROUND: There has been little research on design of studies based on routinely collected data when the clinical endpoint of interest is not recorded, but can be inferred from a prescription. This often happens when exploring the effect of a drug on chronic diseases. Using the LifeLink claims database in studying the possible anti-inflammatory effects of statins in rheumatoid arthritis (RA), oral steroids (OS) were treated as surrogate of inflammatory flare-ups. We compared two cohort study designs, the first using time to event outcomes and the second using quantitative amount of the surrogate.
METHODS: RA patients were extracted from the LifeLink database. In the first study, patients were split into two sub-cohorts based on whether they were using OS within a specified time window of the RA index date (first record of RA). Using Cox models we evaluated the association between time-varying exposure to statins and (i) initiation of OS therapy in the non-users of OS at RA index date and (ii) cessation of OS therapy in the users of OS at RA index date. In the second study, we matched new statin users to non users on age and sex. Zero inflated negative binomial models were used to contrast the number of days’ prescriptions of OS in the year following date of statin initiation for the two exposure groups.
RESULTS: In the unmatched study, the statin exposure hazard ratio (HR) of initiating OS in the 31451 non-users of OS at RA index date was 0.96(95% CI 0.9,1.1) and the statin exposure HR of cessation of OS therapy in the 6026 users of OS therapy at RA index date was 0.95 (0.87,1.05). In the matched cohort of 6288 RA patients the statin exposure rate ratio for duration on OS therapy was 0.88(0.76,1.02). There was digit preference for outcomes in multiples of 7 and 30 days.
CONCLUSIONS: The `time to event’ study design was preferable because it better exploits information on all available patients and provides a degree of robustness toward confounding. We found no convincing evidence that statins reduce inflammation in RA patients.

PMID: 21457565 [PubMed - as supplied by publisher]

Free Full text (PDF) available: http://www.biomedcentral.com/content/pdf/1471-2288-11-36.pdf

CER Scan – Epub Ahead of Print

1. Am J Epidemiol. 2011 Mar 23. [Epub ahead of print]

Invited Commentary: Causation or “noitasuaC”?

Schisterman E, Whitcomb B, Bowers K.

Longitudinal studies are often viewed as the “gold standard” of observational epidemiologic research. Establishing a temporal association is a necessary criterion to identify causal relations. However, when covariates in the causal system vary over time, a temporal association is not straightforward. Appropriate analytical methods may be necessary to avoid confounding and reverse causality. These issues come to light in 2 studies of breastfeeding described in the articles by Al-Sahab et al. (Am J Epidemiol. 2011;173(00):0000-0000) and Kramer et al. (Am J Epidemiol. 2011;173(00):0000-0000) in this issue of the Journal. Breastfeeding has multiple time points and is a behavior that is affected by multiple factors, many of which themselves vary over time. This creates a complex causal system that requires careful scrutiny. The methods presented here may be applicable to a wide range of studies that involve time-varying exposures and time-varying confounders.
PMID: 21430191 [PubMed - as supplied by publisher]

FreeFull text (HTML) available: http://aje.oxfordjournals.org/content/early/2011/03/23/aje.kwq499.long

2. Pharmacoepidemiol Drug Saf. 2011 Mar 10. doi: 10.1002/pds.2098. [Epub ahead of print]

The implications of propensity score variable s election strategies in pharmacoepidemiology: an empirical illustration.

Patrick AR, Schneeweiss S, Brookhart MA, Glynn RJ, Rothman KJ, Avorn J, Stürmer T. Division of Pharmacoepidemiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA. arpatrick@partners.org.

PURPOSE: To examine the effect of variable selection strategies on the performance of propensity score (PS) methods in a study of statin initiation, mortality, and hip fracture assuming a true mortality reduction of <?15% and no effect on hip fracture.
METHODS: We compared seniors initiating statins with seniors initiating glaucoma medications. Out of 202 covariates with a prevalence?>?5%, PS variable selection strategies included none, a priori, factors predicting exposure, and factors predicting outcome. We estimated hazard ratios (HRs) for statin initiation on mortality and hip fracture from Cox models controlling for various PSs.
RESULTS: During 1 year follow-up, 2693 of 55?610 study subjects died and 496 suffered a hip fracture. The crude HR for statin initiators was 0.64 for mortality and 0.46 for hip fracture. Adjusting for the non-parsimonious PS yielded effect estimates of 0.83 (95%CI:0.75-0.93) and 0.72 (95%CI:0.56-0.93). Including in the PS only covariates associated with a greater than 20% increase or reduction in outcome rates yielded effect estimates of 0.84 (95%CI:0.75-0.94) and 0.76 (95%CI:0.61-0.95), which were closest to the effects predicted from randomized trials.
CONCLUSION: Due to the difficulty of pre-specifying all potential confounders of an exposure-outcome association, data-driven approaches to PS variable selection may be useful. Selecting covariates strongly associated with exposure but unrelated to outcome should be avoided, because this may increase bias. Selecting variables for PS based on their association with the outcome may help to reduce such bias.
Copyright © 2011 John Wiley & Sons, Ltd.PMID: 21394812 [PubMed - as supplied by publisher]

3. Am J Epidemiol. 2011 Mar 8. [Epub ahead of print]

Doubly Robust Estimation of Causal Effects.

Funk MJ, Westreich D, Wiesen C, Stürmer T, Brookhart MA, Davidian M.

Doubly robust estimation combines a form of outcome regression with a model for the exposure (i.e., the propensity score) to estimate the causal effect of an exposure on an outcome. When used individually to estimate a causal effect, both outcome regression and propensity score methods are unbiased only if the statistical model is correctly specified. The doubly robust estimator combines these 2 approaches such that only 1 of the 2 models need be correctly specified to obtain an unbiased effect estimator. In this introduction to doubly robust estimators, the authors present a conceptual overview of doubly robust estimation, a simple worked example, results from a simulation study examining performance of estimated and bootstrapped standard errors, and a discussion of the potential advantages and limitations of this method. The supplementary material for this paper, which is posted on the Journal’s Web site (http://aje.oupjournals.org/), includes a demonstration of the doubly robust property (Web Appendix 1) and a description of a SAS macro (SAS Institute, Inc., Cary, North Carolina) for doubly robust estimation, available for download at http://www.unc.edu/~mfunk/dr/.
PMID: 21385832 [PubMed - as supplied by publisher]

FreeFull text (HTML) available: http://aje.oxfordjournals.org/content/173/7/761.long

4. Am J Epidemiol. 2011 Feb 28. [Epub ahead of print]

Methods for Estimating Remission Rates From Cross -Sectional Survey Data: Application and Validation Using Data From a National Migraine Study.

Roy J, Stewart WF.

Knowledge about remission rates can affect treatment decisions and facilitate etiologic discoveries. However, little is known about remission of many chronic episodic disorders, including migraine. This is partly due to the fact that medical records do not fully capture the history of these conditions, since patients might stop seeking care once they no longer have symptoms. For these disorders, remission rates would typically be obtained from prospective observational studies. Prospective studies of remission for chronic episodic conditions are rarely conducted, however, and suffer from many analytical challenges, such as outcome-dependent dropout. Here the authors propose an alternative approach that is appropriate for use with cross-sectional survey data in which reported age of onset was recorded. The authors estimated migraine remission rates using data from a 2004 national survey. They took a Bayesian approach and modeled sex- and age-specific remission rates as a function of incidence and prevalence. The authors found that remission rates were an increasing function of age and were similar for men and women. Follow-up survey data from migraine cases (2005) were used to validate the methods. The remission curves estimated from the validation data were very similar to the ones from the cross-sectional data.
PMID: 21357656 [PubMed - as supplied by publisher]

5. Int J Epidemiol. 2011 Mar 30. [Epub ahead of print]

The Simpson’s paradox unraveled.

Hernán MA, Clayton D, Keiding N. Department of Epidemiology, Harvard School of Public Health, Harvard-MIT Division of Health Sciences and Technology, Boston, MA 02115, USA, Department of Medical Genetics, Cambridge University, Addenbrooke’s Hospital, Cambridge, UK and Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark.

BACKGROUND: In a famous article, Simpson described a hypothetical data example that led to apparently paradoxical results.
METHODS: We make the causal structure of Simpson’s example explicit.
RESULTS: We show how the paradox disappears when the statistical analysis is appropriately guided by subject-matter knowledge. We also review previous explanations of Simpson’s paradox that attributed it to two distinct phenomena: confounding and non-collapsibility.
CONCLUSION: Analytical errors may occur when the problem is stripped of its causal context and analyzed merely in statistical terms.

PMID: 21454324 [PubMed - as supplied by publisher]

Free Full text (PDF) available: http://ije.oxfordjournals.org/content/early/2011/03/30/ije.dyr041.full.pdf+html

6. Clin Trials. 2011 Jan 31. [Epub ahead of print]

Bayesian models for subgroup analysis in clinical trials.

Jones HE, Ohlssen DI, Neuenschwander B, Racine A, Branson M. School of Social and Community Medicine, University of Bristol, UK.

BACKGROUND: In a pharmaceutical drug development setting, possible interactions between the treatment and particular baseline clinical or demographic factors are often of interest. However, the subgroup analysis required to investigate such associations remains controversial. Concerns with classical hypothesis testing approaches to the problem include low power, multiple testing, and the possibility of data dredging.
PURPOSE: As an alternative to hypothesis testing, the use of shrinkage estimation techniques is investigated in the context of an exploratory post hoc subgroup analysis. A range of models that have been suggested in the literature are reviewed. Building on this, we explore a general modeling strategy, considering various options for shrinkage of effect estimates. This is applied to a case-study, in which evidence was available from seven-phase II-III clinical trials examining a novel therapy, and also to two artificial datasets with the same structure.
METHODS: Emphasis is placed on hierarchical modeling techniques, adopted within a Bayesian framework using freely available software. A range of possible subgroup model structures are applied, each incorporating shrinkage estimation techniques.
RESULTS: The investigation of the case-study showed little evidence of subgroup effects. Because inferences appeared to be consistent across a range of well-supported models, and model diagnostic checks showed no obvious problems, it seemed this conclusion was robust. It is reassuring that the structured shrinkage techniques appeared to work well in a situation where deeper inspection of the data suggested little evidence of subgroup effects.
LIMITATIONS: The post hoc examination of subgroups should be seen as an exploratory analysis, used to help make better informed decisions regarding potential future studies examining specific subgroups. To a certain extent, the degree of understanding provided by such assessments will be limited by the quality and quantity of available data.

CONCLUSIONS: In light of recent interest by health authorities into the use of subgroup analysis in the context of drug development, it appears that Bayesian approaches involving shrinkage techniques could play an important role in this area. Hopefully, the developments outlined here provide useful methodology for tackling such a problem, in-turn leading to better informed decisions regarding subgroups.

PMID: 21282293 [PubMed - as supplied by publisher]

7. Clin Trials. 2011 Jan 26. [Epub ahead of print]

Challenges in the design and conduct of controlled clinical effectiveness trials in schizophrenia.

Rosenheck RA, Krystal JH, Lew R, Barnett PG, Thwin SS, Fiore L, Valley D, Huang GD, Neal C, Vertrees JE, Liang MH. Veterans Affairs (VA) Connecticut Healthcare System, West Haven, CT, USA, Yale School of Medicine, New Haven, CT, USA.

BACKGROUND: The introduction of antipsychotic medication has been a major advance in the treatment of schizophrenia and allows millions of people to live outside of institutions. It is generally believed that long-acting intramuscular antipsychotic medication is the most effective approach to increasing medication adherence and thereby reduce relapse in high-risk patients with schizophrenia, but the data are scant.
PURPOSE: To report the design of a study to assess the effect of long-acting injectable risperidone in unstable patients and under more realistic conditions than previously studied and to evaluate the effect of this medication on psychiatric inpatient hospitalization, schizophrenia symptoms, quality of life, medication adherence, side effects, and health care costs.
METHODS: The trial was an open randomized clinical comparative effectiveness trial in patients with schizophrenia or schizo-affective disorders in which parenteral risperidone was compared to an oral antipsychotic regimen selected by each control patient’s psychiatrist. Participants had unstable psychiatric disease defined by recent hospitalization or exhibition of unusual need for psychiatric services. The primary endpoint was hospitalization for psychiatric indications; the secondary endpoint was psychiatric symptoms.
RESULTS: Overall, 382 patients were randomized. Determination of a persons’ competency to understand the elements of informed consent was addressed. The use of a closed-circuit TV interview for psychosocial measures provided an economical, high quality, reliable means of collecting data. A unique method for insuring that usual care was optimal was incorporated in the follow-up of all subjects.
LIMITATIONS: Patients with schizophrenia or schizo-affective disorders and with the common co-morbid illnesses seen in the VA are a challenging group of subjects to study in long-term trials. Some techniques unique in the VA and found useful may not be generalizable or applicable in other research or treatment settings.
CONCLUSIONS: The trial tested a new antipsychotic medication early in its adoption in the Veterans Health Administration. The VA has a unique electronic medical record and database which can be used to identify the endpoint, that is, first hospitalization due to a psychiatric problem, with complete ascertainment. Several methodologic solutions addressed competency to understand elements of consent, the costs and reliability of collecting interview data gathering, and insuring usual care.
PMID: 21270143 [PubMed - as supplied by publisher]

Author Scan – Published within the past 30 days/ Epub Ahead of Print

1. PLoS One. 2011 Mar 22;6(3):e18062.

Multicenter Evaluation of a Novel Surveillance Paradigm for Complications of Mechanical V entilation.

Klompas M, Khan Y, Kleinman K, Evans RS, Lloyd JF, Stevenson K, Samore M, Platt R; for the CDC Prevention Epicenters Program. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States of America.

BACKGROUND: Ventilator-associated pneumonia (VAP) surveillance is time consuming, subjective, inaccurate, and inconsistently predicts outcomes. Shifting surveillance from pneumonia in particular to complications in general might circumvent the VAP definition’s subjectivity and inaccuracy, facilitate electronic assessment, make interfacility comparisons more meaningful, and encourage broader prevention strategies. We therefore evaluated a novel surveillance paradigm for ventilator-associated complications (VAC) defined by sustained increases in patients’ ventilator settings after a period of stable or decreasing support.
METHODS: We assessed 600 mechanically ventilated medical and surgical patients from three hospitals. Each hospital contributed 100 randomly selected patients ventilated 2-7 days and 100 patients ventilated >7 days. All patients were independently assessed for VAP and for VAC. We compared incidence-density, duration of mechanical ventilation, intensive care and hospital lengths of stay, hospital mortality, and time required for surveillance for VAP and for VAC. A subset of patients with VAP and VAC were independently reviewed by a physician to determine possible etiology.
RESULTS: Of 597 evaluable patients, 9.3% had VAP (8.8 per 1,000 ventilator days) and 23% had VAC (21.2 per 1,000 ventilator days). Compared to matched controls, both VAP and VAC prolonged days to extubation (5.8, 95% CI 4.2-8.0 and 6.0, 95% CI 5.1-7.1 respectively), days to intensive care discharge (5.7, 95% CI 4.2-7.7 and 5.0, 95% CI 4.1-5.9), and days to hospital discharge (4.7, 95% CI 2.6-7.5 and 3.0, 95% CI 2.1-4.0). VAC was associated with increased mortality (OR 2.0, 95% CI 1.3-3.2) but VAP was not (OR 1.1, 95% CI 0.5-2.4). VAC assessment was faster (mean 1.8 versus 39 minutes per patient). Both VAP and VAC events were predominantly attributable to pneumonia, pulmonary edema, ARDS, and atelectasis.
CONCLUSIONS: Screening ventilator settings for VAC captures a similar set of complications to traditional VAP surveillance but is faster, more objective, and a superior predictor of outcomes.
PMID: 21445364 [PubMed - as supplied by publisher]

Free Full text (PDF) available: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3062570/pdf/pone.0018062.pdf

2. Med Care. 2011 Mar 18. [Epub ahead of print]

Placebo Adherence, Clinical Outcomes, and Mortality in the Women’s Health Initiative Randomized Hormone Therapy Trials.

R Curtis J, Larson JC, Delzell E, Brookhart MA, Cadarette SM, Chlebowski R, Judd S, Safford M, Solomon DH, Lacroix AZ. * Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL † Fred Hutchinson Cancer Research Center, Seattle, WA ‡ Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL § Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC ? Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada ¶ Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA ? Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham, AL ** Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Boston, MA.

BACKGROUND: Medication adherence may be a proxy for healthy behaviors and other factors that affect outcomes. Prior studies of the association between placebo adherence and health outcomes have been limited primarily to men enrolled in clinical trials and cardiovascular disease outcomes. We examined associations between adherence to placebo and the risk of fracture, coronary heart disease, cancer, and all-cause mortality in the 2 Women’s Health Initiative hormone therapy randomized trials.
METHODS: Postmenopausal women randomized to placebo with adherence measured at least once were eligible for analysis. Time-varying adherence was assessed by dispensing history and pill counts. Outcome adjudication was based on physician review of medical records. Cox proportional hazards models evaluated the relation between high adherence (=80%) to placebo and various outcomes, referent to low adherence (<80%).
RESULTS: A total of 13,444 postmenopausal women were under observation for 106,066 person-years. High placebo adherence was inversely associated with most outcomes including hip fracture [hazard ratio (HR), 0.50; 95% confidence interval (CI), 0.33-0.78], myocardial infarction (HR, 0.69; 95% CI, 0.50-0.95), cancer death (HR, 0.60; 95% CI, 0.43-0.82), and all-cause mortality (HR, 0.64; 95% CI, 0.51-0.80) after adjustment for potential confounders. Women with low adherence to placebo were 20% more likely to have low adherence to statins and osteoporosis medications.
CONCLUSIONS: In the Women’s Health Initiative clinical trials, high adherence to placebo was associated with favorable clinical outcomes and mortality. Until the healthy behaviors and/or other factors for which high adherence is a proxy can be better elucidated, caution is warranted when interpreting the magnitude of benefit of medication adherence.
PMID: 21422960 [PubMed - as supplied by publisher]

3. Health Serv Res. 2011 Mar 17. doi: 10.1111/j.1475-6773.2011.01253.x. [Epub ahead of print]

Crowd-out and Exposure Effects of Physical Comorbidities on Mental Healt h Care Use: Implications for Racial-Ethnic Disparities in Access. Lê Cook B, McGuire TG, Alegría M, Normand SL. Center for Multicultural Mental Health Research, 120 Beacon St., 4th Floor, Somerville, MA 02143 Department of Psychiatry, Harvard Medical School, Boston, MA Department of Health Care Policy, Harvard Medical School, Boston, MA Center for Multicultural Mental Health Research, Somerville, MA.

Objectives. In disparities models, researchers adjust for differences in “clinical need,” including indicators of comorbidities. We reconsider this practice, assessing (1) if and how having a comorbidity changes the likelihood of recognition and treatment of mental illness; and (2) differences in mental health care disparities estimates with and without adjustment for comorbidities.
Data. Longitudinal data from 2000 to 2007 Medical Expenditure Panel Survey (n=11,083) split into pre and postperiods for white, Latino, and black adults with probable need for mental health care.
Study Design. First, we tested a crowd-out effect (comorbidities decrease initiation of mental health care after a primary care provider [PCP] visit) using logistic regression models and an exposure effect (comorbidities cause more PCP visits, increasing initiation of mental health care) using instrumental variable methods. Second, we assessed the impact of adjustment for comorbidities on disparity estimates.
Principal Findings. We found no evidence of a crowd-out effect but strong evidence for an exposure effect. Number of postperiod visits positively predicted initiation of mental health care. Adjusting for racial/ethnic differences in comorbidities increased black-white disparities and decreased Latino-white disparities.
Conclusions. Positive exposure findings suggest that intensive follow-up programs shown to reduce disparities in chronic-care management may have additional indirect effects on reducing mental health care disparities.
© Health Research and Educational Trust.PMID: 21413984 [PubMed - as supplied by publisher]

March 2011

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CER Scan [Published within the last 30 days]

1. Med Care. 2011 Mar;49(3):257-266.

Predicting the Risk of 1-Year Mortality in Incident Dialysis Patients: Accounting for Case-Mix Severity in Studies Using Administrative Data.

Quinn RR, Laupacis A, Hux JE, Oliver MJ, Austin PC.
BACKGROUND: Administrative databases are increasingly being used to study the incident dialysis population and have important advantages. However, traditional methods of risk adjustment have limitations in this patient population.
OBJECTIVE: Our objective was to develop a prognostic index for 1-year mortality in incident dialysis patients using administrative data that was applicable to ambulatory patients, used objective definitions of candidate predictor variables, and was easily replicated in other environments.
RESEARCH DESIGN: Anonymized, administrative health data housed at the Institute for Clinical Evaluative Sciences in Toronto, Canada were used to identify a population-based sample of 16,205 patients who initiated dialysis between July 1, 1998 and March 31, 2005. The cohort was divided into derivation, validation, and testing samples and 4 different strategies were used to derive candidate logistic regression models for 1-year mortality. The final risk prediction model was selected based on discriminatory ability (as measured by the c-statistic) and a risk prediction score was derived using methods adopted from the Framingham Heart Study. Calibration of the predictive model was assessed graphically.
RESULTS: The risk of death during the first year of dialysis therapy was 16.4% in the derivation sample. The final model had a c-statistic of 0.765, 0.763, and 0.756 in the derivation, validation, and testing samples, respectively. Plots of actual versus predicted risk of death at 1-year showed good calibration.
CONCLUSION: The prognostic index and summary risk score accurately predict 1-year mortality in incident dialysis patients and can be used for the purposes of risk adjustment.
Copyright © 2011 John Wiley & Sons, Ltd.
PMID: 21301370 [PubMed - as supplied by publisher]

CER Scan [Epub Ahead of Print]

1. Stat Med. 2011 Feb 24. doi: 10.1002/sim.4168. [Epub ahead of print]

Generalized propensity score for estimating the average treatment effect of multiple treatments.

Feng P, Zhou XH, Zou QM, Fan MY, Li XS.

The propensity score method is widely used in clinical studies to estimate the effect of a treatment with two levels on patient’s outcomes. However, due to the complexity of many diseases, an effective treatment often involves multiple components. For example, in the practice of Traditional Chinese Medicine (TCM), an effective treatment may include multiple components, e.g. Chinese herbs, acupuncture, and massage therapy. In clinical trials involving TCM, patients could be randomly assigned to either the treatment or control group, but they or their doctors may make different choices about which treatment component to use. As a result, treatment components are not randomly assigned. Rosenbaum and Rubin proposed the propensity score method for binary treatments, and Imbens extended their work to multiple treatments. These authors defined the generalized propensity score as the conditional probability of receiving a particular level of the treatment given the pre-treatment variables. In the present work, we adopted this approach and developed a statistical methodology based on the generalized propensity score in order to estimate treatment effects in the case of multiple treatments. Two methods were discussed and compared: propensity score regression adjustment and propensity score weighting. We used these methods to assess the relative effectiveness of individual treatments in the multiple-treatment IMPACT clinical trial. The results reveal that both methods perform well when the sample size is moderate or large. Copyright © 2011 John Wiley & Sons, Ltd.
PMID: 21351291 [PubMed - as supplied by publisher]

2. Eur J Epidemiol. 2011 Feb 23. [Epub ahead of print]

Estimating measures of interaction on an additive scale for preventive exposures.
Knol MJ, Vanderweele TJ, Groenwold RH, Klungel OH, Rovers MM, Grobbee DE.
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands, m.j.knol@umcutrecht.nl.
Measures of interaction on an additive scale (relative excess risk due to interaction [RERI], attributable proportion [AP], synergy index [S]), were developed for risk factors rather than preventive factors. It has been suggested that preventive factors should be recoded to risk factors before calculating these measures. We aimed to show that these measures are problematic with preventive factors prior to recoding, and to clarify the recoding method to be used to circumvent these problems. Recoding of preventive factors should be done such that the stratum with the lowest risk becomes the reference category when both factors are considered jointly (rather than one at a time). We used data from a case-control study on the interaction between ACE inhibitors and the ACE gene on incident diabetes. Use of ACE inhibitors was a preventive factor and DD ACE genotype was a risk factor. Before recoding, the RERI, AP and S showed inconsistent results (RERI = 0.26 [95%CI: -0.30; 0.82], AP = 0.30 [95%CI: -0.28; 0.88], S = 0.35 [95%CI: 0.02; 7.38]), with the first two measures suggesting positive interaction and the third negative interaction. After recoding the use of ACE inhibitors, they showed consistent results (RERI = -0.37 [95%CI: -1.23; 0.49], AP = -0.29 [95%CI: -0.98; 0.40], S = 0.43 [95%CI: 0.07; 2.60]), all indicating negative interaction. Preventive factors should not be used to calculate measures of interaction on an additive scale without recoding.
PMID: 21344323 [PubMed - as supplied by publisher]

3. Stat Med. 2011 Feb 21. doi: 10.1002/sim.4200. [Epub ahead of print]

Comparing paired vs non-paired statistical methods of analyses when making inferences about absolute risk reductions in propensity-score matched samples.

Austin PC.
Propensity-score matching allows one to reduce the effects of treatment-selection bias or confounding when estimating the effects of treatments when using observational data. Some authors have suggested that methods of inference appropriate for independent samples can be used for assessing the statistical significance of treatment effects when using propensity-score matching. Indeed, many authors in the applied medical literature use methods for independent samples when making inferences about treatment effects using propensity-score matched samples. Dichotomous outcomes are common in healthcare research. In this study, we used Monte Carlo simulations to examine the effect on inferences about risk differences (or absolute risk reductions) when statistical methods for independent samples are used compared with when statistical methods for paired samples are used in propensity-score matched samples. We found that compared with using methods for independent samples, the use of methods for paired samples resulted in: (i) empirical type I error rates that were closer to the advertised rate; (ii) empirical coverage rates of 95 per cent confidence intervals that were closer to the advertised rate; (iii) narrower 95 per cent confidence intervals; and (iv) estimated standard errors that more closely reflected the sampling variability of the estimated risk difference. Differences between the empirical and advertised performance of methods for independent samples were greater when the treatment-selection process was stronger compared with when treatment-selection process was weaker. We recommend using statistical methods for paired samples when using propensity-score matched samples for making inferences on the effect of treatment on the reduction in the probability of an event occurring. Copyright © 2011 John Wiley & Sons, Ltd.

PMID: 21337595 [PubMed - as supplied by publisher]

4. Int J Epidemiol. 2011 Feb 9. [Epub ahead of print]

Commentary: Can ‘many weak’ instruments ever be ‘strong’? Sheehan NA, Didelez V.
Department of Health Sciences, University of Leicester and Department of Mathematics, University of Bristol, Bristol, UK.
PMID: 21310719 [PubMed - as supplied by publisher]
5. Stat Methods Med Res. 2011 Feb 7. [Epub ahead of print]

Comparing measurement error correction methods for rate-of-change exposure variables in survival analysis.

Veronesi G, Ferrario MM, Chambless LE.
In this article we focus on comparing measurement error correction methods for rate-of-change exposure variables in survival analysis, when longitudinal data are observed prior to the follow-up time. Motivational examples include the analysis of the association between changes in cardiovascular risk factors and subsequent onset of coronary events. We derive a measurement error model for the rate of change, estimated through subject-specific linear regression, assuming an additive measurement error model for the time-specific measurements. The rate of change is then included as a time-invariant variable in a Cox proportional hazards model, adjusting for the first time-specific measurement (baseline) and an error-free covariate. In a simulation study, we compared bias, standard deviation and mean squared error (MSE) for the regression calibration (RC) and the simulation-extrapolation (SIMEX) estimators. Our findings indicate that when the amount of measurement error is substantial, RC should be the preferred method, since it has smaller MSE for estimating the coefficients of the rate of change and of the variable measured without error. However, when the amount of measurement error is small, the choice of the method should take into account the event rate in the population and the effect size to be estimated. An application to an observational study, as well as examples of published studies where our model could have been applied, are also provided.
PMID: 21300627 [PubMed - as supplied by publisher]

5. Clin Trials. 2011 Jan 31. [Epub ahead of print]

Bayesian models for subgroup analysis in clinical trials.

Jones HE, Ohlssen DI, Neuenschwander B, Racine A, Branson M.
BACKGROUND: In a pharmaceutical drug development setting, possible interactions between the treatment and particular baseline clinical or demographic factors are often of interest. However, the subgroup analysis required to investigate such associations remains controversial. Concerns with classical hypothesis testing approaches to the problem include low power, multiple testing, and the possibility of data dredging.
PURPOSE: As an alternative to hypothesis testing, the use of shrinkage estimation techniques is investigated in the context of an exploratory post hoc subgroup analysis. A range of models that have been suggested in the literature are reviewed. Building on this, we explore a general modeling strategy, considering various options for shrinkage of effect estimates. This is applied to a case-study, in which evidence was available from seven-phase II-III clinical trials examining a novel therapy, and also to two artificial datasets with the same structure.
METHODS: Emphasis is placed on hierarchical modeling techniques, adopted within a Bayesian framework using freely available software. A range of possible subgroup model structures are applied, each incorporating shrinkage estimation techniques.
RESULTS: The investigation of the case-study showed little evidence of subgroup effects. Because inferences appeared to be consistent across a range of well-supported models, and model diagnostic checks showed no obvious problems, it seemed this conclusion was robust. It is reassuring that the structured shrinkage techniques appeared to work well in a situation where deeper inspection of the data suggested little evidence of subgroup effects.
LIMITATIONS: The post hoc examination of subgroups should be seen as an exploratory analysis, used to help make better informed decisions regarding potential future studies examining specific subgroups. To a certain extent, the degree of understanding provided by such assessments will be limited by the quality and quantity of available data.
CONCLUSIONS: In light of recent interest by health authorities into the use of subgroup analysis in the context of drug development, it appears that Bayesian approaches involving shrinkage techniques could play an important role in this area. Hopefully, the developments outlined here provide useful methodology for tackling such a problem, in-turn leading to better informed decisions regarding subgroups.
PMID: 21282293 [PubMed - as supplied by publisher]

6. Int J Epidemiol. 2011 Jan 13. [Epub ahead of print]

Commentary: Adjusting for bias: a user’s guide to performing plastic surgery on meta-analyses of observational studies.

Ioannidis JP.

Department of Medicine, Stanford Prevention Research Center, Stanford University School of Medicine, MSOB X306, 251 Campus Drive, Stanford, CA 94305, USA. jioannid@stanford.edu.
PMID: 21233141 [PubMed - as supplied by publisher]

7. Int J Epidemiol. 2010 Dec 23. [Epub ahead of print]

A proposed method of bias adjustment for meta-analyses of published observational studies.
Thompson S, Ekelund U, Jebb S, Lindroos AK, Mander A, Sharp S, Turner R, Wilks D.
MRC Biostatistics Unit, Cambridge, UK, MRC Epidemiology Unit, Cambridge, UK and MRC Human Nutrition Research, Cambridge, UK.
OBJECTIVE: Interpretation of meta-analyses of published observational studies is problematic because of numerous sources of bias. We develop bias assessment, elicitation and adjustment methods, and apply them to a systematic review of longitudinal observational studies of the relationship between objectively

measured physical activity and subsequent change in adiposity in children.
METHODS: We separated internal biases that reflect study quality from external biases that reflect generalizability to a target setting. Since published results were presented in different formats, these were all converted to correlation coefficients. Biases were considered as additive or proportional on the

correlation scale. Opinions about the extent of each bias in each study, together with its uncertainty, were elicited in a formal process from quantitatively trained assessors for the internal biases and subject-matter specialists for the external biases. Bias-adjusted results for each study were combined across assessors using median pooling, and results combined across studies by random-effects meta-analysis.
RESULTS: Before adjusting for bias, the pooled correlation is difficult to interpret because the studies varied substantially in quality and design, and there was considerable heterogeneity. After adjusting for both the internal and external biases, the pooled correlation provides a meaningful quantitative summary of all available evidence, and the confidence interval incorporates the elicited uncertainties about the extent of the biases. In the adjusted meta-analysis, there was no apparent heterogeneity.
CONCLUSION: This approach provides a viable method of bias adjustment for meta-analyses of observational studies, allowing the quantitative synthesis of evidence from otherwise incompatible studies. From the meta-analysis of longitudinal observational studies, we conclude that there is no evidence that physical activity is associated with gain in body fat.
PMID: 21186183 [PubMed - as supplied by publisher]
Free Full Text: http://ije.oxfordjournals.org/content/early/2010/12/23/ije.dyq248.full.pdf+html

Author Scan

1. Am J Epidemiol. 2011 Mar 1;173(5):569-77. Epub 2011 Feb 2.

Limitation of Inverse Probability-of-Censoring Weights in Estimating Survival in the Presence of Strong Selection Bias.

Howe CJ, Cole SR, Chmiel JS, Muñoz A.
In time-to-event analyses, artificial censoring with correction for induced selection bias using inverse probability-of-censoring weights can be used to 1) examine the natural history of a disease after effective interventions are widely available, 2) correct bias due to noncompliance with fixed or dynamic treatment regimens, and 3) estimate survival in the presence of competing risks. Artificial censoring entails censoring participants when they meet a predefined study criterion, such as exposure to an intervention, failure to comply, or the occurrence of a competing outcome. Inverse probability-of-censoring weights use measured common predictors of the artificial censoring mechanism and the outcome of interest to determine what the survival experience of the artificially censored participants would be had they never been exposed to the intervention, complied with their treatment regimen, or not developed the competing outcome. Even if all common predictors are appropriately measured and taken into account, in the context of small sample size and strong selection bias, inverse probability-of-censoring weights could fail because of violations in assumptions necessary to correct selection bias. The authors used an example from the Multicenter AIDS Cohort Study, 1984-2008, regarding estimation of long-term acquired immunodeficiency syndrome-free survival to demonstrate the impact of violations in necessary assumptions. Approaches to improve correction methods are discussed.
PMID: 21289029 [PubMed - in process]

2. Stat Modelling. 2010 Dec;10(4):421-439.

A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use.

Neelon BH, O’Malley AJ, Normand SL.
Department of Health Care Policy, Harvard Medical School, Boston, USA.
In applications involving count data, it is common to encounter an excess number of zeros. In the study of outpatient service utilization, for example, the number of utilization days will take on integer values, with many subjects having no utilization (zero values). Mixed-distribution models, such as the zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB), are often used to fit such data. A more general class of mixture models, called hurdle models, can be used to model zero-deflation as well as zero-inflation. Several authors have proposed frequentist approaches to fitting zero-inflated models for repeated measures. We describe a practical Bayesian approach which incorporates prior information, has optimal small-sample properties, and allows for tractable inference. The approach can be easily implemented using standard Bayesian software. A study of psychiatric outpatient service use illustrates the methods.
PMCID: PMC3039917 [Available on 2011/12/1], PMID: 21339863 [PubMed]