You can find them all here.

CER Scan [Epub ahead of print]

A pilot ‘cohort multiple randomised controlled trial’ of treatment by [...]]]> The DEcIDE Methods Center publishes a monthly literature scan of current articles of interest to the field of comparative effectiveness research.

You can find them all here.

CER Scan [Epub ahead of print]

1. Contemp Clin Trials. 2012 Apr 20. [Epub ahead of print]

A pilot ‘cohort multiple randomised controlled trial’ of treatment by a homeopath for women with menopausal hot flushes. Relton C, O’Cathain A, Nicholl J.

INTRODUCTION: In order to address the limitations of the standard pragmatic RCT design, the innovative ‘cohort multiple RCT’ design was developed. The design was first piloted by addressing a clinical question ” What is the clinical and cost effectiveness of treatment by a homeopath for women with menopausal hot flushes?”. METHODS: A cohort with the condition of interest (hot flushes) was recruited through an observational study of women’s midlife health and consented  to provide observational data and have their data used comparatively. The ‘Hot Flush’ Cohort were then screened in order to identify patients eligible for a trial of the offer of treatment by a homeopath (Eligible Trial Group). A proportion of the Eligible Trial Group was then randomly selected to the Offer Group and offered treatment. A “patient centred” approach to information and consent was adopted. Patients were not (i) told about treatments that they would not be offered, and trial intervention information was only given to the Offer Group after random selection. Patients were not (ii) given prior information that their treatment would be decided by chance. RESULTS: The ‘cohort multiple RCT’ design was acceptable to the NHS Research Ethics Committee. The majority of patients completed multiple questionnaires. Acceptance of the offer was high (17/24). DISCUSSION: This pilot identified the feasibility of an innovative design in practice. Further research is required to test the concept of undertaking multiple trials within a cohort of patients and to assess the acceptability of the “patient centred” approach to information and consent. Copyright © 2012 Elsevier Inc. All rights reserved.

PMID: 22551742  [PubMed - as supplied by publisher]

LINK: http://www.sciencedirect.com/science/article/pii/S1551714412000973

2. Am J Epidemiol. 2012 Apr 17. [Epub ahead of print]

Comparison of Instrumental Variable Analysis Using a New Instrument With Risk Adjustment Methods to Reduce Confounding by Indication. Fang G, Brooks JM, Chrischilles EA.

Confounding by indication is a vexing problem, especially in evaluating treatment effects using observational data, since treatment decisions are often related to disease severity, prognosis, and frailty. To compare the ability of the instrumental variable (IV) approach with a new instrument based on the local-area practice style and risk adjustment methods, including conventional multivariate regression and propensity score adjustment, to reduce confounding by indication, the authors investigated the effects of long-term control (LTC) therapy on the occurrence of acute asthma exacerbation events among children and young adults with incident and uncontrolled persistent asthma, using Iowa Medicaid claims files from 1997-1999. Established evidence from clinical trials has demonstrated the protective benefits of LTC therapy for persistent asthma. Among patients identified (n = 4,275), those with higher asthma severity at baseline were more likely to receive LTC therapy. The multivariate regression and propensity score adjustment methods suggested that LTC therapy had no effect on the occurrence of acute exacerbation events. Estimates from the new IV approach showed that LTC therapy significantly decreased the occurrence of acute exacerbation events, which is consistent with established clinical evidence. The authors discuss how to interpret estimates from the risk adjustment and IV methods when the treatment effect is heterogeneous.

PMID: 22510277  [PubMed - as supplied by publisher]

LINK: http://aje.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=22510277

3. Epidemiology. 2012 Apr 10. [Epub ahead of print]

Estimating the Effects of Multiple Time-varying Exposures Using Joint Marginal Structural Models: Alcohol Consumption, Injection Drug Use, and HIV Acquisition. Howe CJ, Cole SR, Mehta SH, Kirk GD. Department of Epidemiology, Center for Population Health and Clinical Epidemiology, Brown University Program in Public Health, Providence, RI; Department of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD.

The joint effects of multiple exposures on an outcome are frequently of interest in epidemiologic research. In 2001, Hernán et al (J Am Stat Assoc. 2001;96:440-448) presented methods for estimating the joint effects of multiple time-varying exposures subject to time-varying confounding affected by prior exposure using joint marginal structural models. Nonetheless, the use of these joint models is rare in the applied literature. Minimal uptake of these joint models, in contrast to the now widely used standard marginal structural model, is due in part to a lack of examples demonstrating the method. In this paper, we review the assumptions necessary for unbiased estimation of joint effects as well as the distinction between interaction and effect measure modification. We demonstrate the use of marginal structural models for estimating the joint effects of alcohol consumption and injection drug use on HIV acquisition, using data from 1525 injection drug users in the AIDS Link to Intravenous Experience cohort study. In the joint model, the hazard ratio (HR) for heavy drinking in the absence of any drug injections was 1.58 (95% confidence interval = 0.67-3.73). The HR for any drug injections in the absence of heavy drinking was 1.78 (1.10-2.89). The HR for heavy drinking and any drug injections was 2.45 (1.45-4.12). The P values for multiplicative and additive interaction were 0.7620 and 0.9200, respectively, indicating a lack of departure from effects that multiply or add. We could not rule out interaction on either scale due to imprecision.

PMID: 22495473  [PubMed - as supplied by publisher]

LINK: http://journals.lww.com/epidem/Abstract/publishahead/Estimating_the_Effects_of_Multiple_Time_varying.99490.aspx

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

Sample size and power calculations for medical studies by simulation when closed form expressions are not available. Landau S, Stahl D. King’s College London, Institute of Psychiatry, Department of Biostatistics, London, UK.

This paper shows how Monte Carlo simulation can be used for sample size, power or precision calculations when planning medical research studies. Standard study designs can lead to the use of analysis methods for which power formulae do not exist. This may be because complex modelling techniques with optimal statistical properties are used but power formulae have not yet been derived or because analysis models are employed that divert from the population model due to lack of availability of more appropriate analysis tools. Our presentation concentrates on the conceptual steps involved in carrying out power or precision calculations by simulation. We demonstrate these steps in three examples concerned with (i) drop out in longitudinal studies, (ii) measurement error in observational studies and (iii) causal effect estimation in randomised controlled trials with non-compliance. We conclude that the Monte Carlo simulation approach is an important general tool in the methodological arsenal for assessing power and precision.

PMID: 22491174  [PubMed - as supplied by publisher]

LINK: http://smm.sagepub.com/content/early/2012/04/04/0962280212439578.long

CER Scan [published within the last 30 days]

1. BMC Med Res Methodol. 2012 Apr 10;12(1):46. [Epub ahead of print]

Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods. Seaman SR, Bartlett JW, White IR.

BACKGROUND: Multiple imputation is often used for missing data. When a  model contains as covariates more than one function of a variable, it is not obvious how best to impute missing values in these covariates. Consider a regression with outcome Y and covariates X and X^2. In ‘passive imputation’ a value X* is imputed for X and then X^2 is imputed as (X*)^2. A recent proposal is to treat X^2 as ‘just another variable’ (JAV) and impute X and X^2 under multivariate normality. METHODS: We use simulation to investigate the performance of three methods that can easily be implemented in standard software: 1) linear regression of X on Y to impute X then passive imputation of X^2; 2) the same regression but with predictive mean matching (PMM); and 3) JAV. We also investigate the performance of analogous methods when the analysis involves an interaction, and study the theoretical properties of JAV. The application of the methods when complete or incomplete confounders are also present is illustrated using data from the EPIC Study. RESULTS: JAV gives consistent estimation when the analysis is linear regression with a quadratic or interaction term and X is missing completely at random. When X is missing at random, JAV may be biased, but this bias is generally less than for passive imputation and PMM. Coverage for JAV was usually good when bias was small. However, in some scenarios with a more pronounced quadratic effect, bias was large and coverage poor. When the analysis was logistic regression, JAV’s performance was sometimes very poor. PMM generally improved on passive imputation, in terms of bias and coverage, but did not eliminate the bias. CONCLUSIONS: Given the current state of available software, JAV is the best of a set of imperfect imputation methods for linear regression with a quadratic or interaction effect, but should not be used for logistic regression.

PMID: 22489953  [PubMed - as supplied by publisher]

Available Open-Access: http://www.biomedcentral.com/content/pdf/1471-2288-12-46.pdf

2. Value Health. 2012 Mar-Apr;15(2):217-30.

Prospective observational studies to assess comparative effectiveness: the ISPOR  good research practices task force report. Berger ML, Dreyer N, Anderson F, Towse A, Sedrakyan A, Normand SL.  OptumInsight, Life Sciences, New York, NY 10026, USA. Marc.Berger@Optum.com

OBJECTIVE: In both the United States and Europe there has been an increased interest in using comparative effectiveness research of interventions to inform health policy decisions. Prospective observational studies will undoubtedly be conducted with increased frequency to assess the comparative effectiveness of different treatments, including as a tool for “coverage with evidence development,” “risk-sharing contracting,” or key element in a “learning health-care system.” The principle alternatives for comparative effectiveness research include retrospective observational studies, prospective observational studies, randomized clinical trials, and naturalistic (“pragmatic”) randomized clinical trials.

METHODS: This report details the recommendations of a Good Research Practice Task Force on Prospective Observational Studies for comparative effectiveness research. Key issues discussed include how to decide when to do a prospective observational study in light of its advantages and disadvantages with respect to alternatives, and the report summarizes the challenges and approaches to the appropriate design, analysis, and execution of prospective observational studies to make them most valuable and relevant to health-care decision makers.

RECOMMENDATIONS: The task force emphasizes the need for precision and clarity in specifying the key policy questions to be addressed and that studies should be designed with a goal of drawing causal inferences whenever possible. If a study is being performed to support a policy decision, then it should be designed as hypothesis testing-this requires drafting a protocol as if subjects were to be randomized and that investigators clearly state the purpose or main hypotheses, define the treatment groups and outcomes, identify all measured and unmeasured confounders, and specify the primary analyses and required sample size. Separate from analytic and statistical approaches, study design choices may strengthen the ability to address potential biases and confounding in prospective observational studies. The use of inception cohorts, new user designs, multiple comparator groups, matching designs, and assessment of outcomes thought not to be impacted by the therapies being compared are several strategies that should be given strong consideration recognizing that there may be feasibility constraints. The reasoning behind all study design and analytic choices should be transparent and explained in study protocol. Execution of prospective observational studies is as important as their design and analysis in ensuring that results are valuable and relevant, especially capturing the target population of interest, having reasonably complete and nondifferential follow-up. Similar to the concept of the importance of declaring a prespecified hypothesis, we believe that the credibility of many prospective observational studies would be enhanced by their registration on appropriate publicly accessible sites (e.g., clinicaltrials.gov and encepp.eu) in advance of their execution. Copyright © 2012 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

PMID: 22433752  [PubMed - in process]

LINK: http://www.valueinhealthjournal.com/article/S1098-3015(12)00007-1/abstract

3. Pharmacoepidemiol Drug Saf. 2012 May 2. doi: 10.1002/pds.3284. [Epub ahead of print]

Algorithms to estimate the beginning of pregnancy in administrative databases. Margulis AV, Setoguchi S, Mittleman MA, Glynn RJ, Dormuth CR, Hernández-Díaz S. Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Boston, MA, USA. andreamargulis@post.harvard.edu.

PURPOSE: The role of administrative databases for research on drug safety during pregnancy can be limited by their inaccurate assessment of the timing of exposure, as the gestational age at birth is typically unavailable. Therefore, we sought to develop and validate algorithms to estimate the gestational age at birth using information available in these databases. METHODS: Using a population-based cohort of 286,432 mother-child pairs in British Columbia (1998-2007), we validated an ICD-9/10-based preterm-status indicator and developed algorithms to estimate the gestational age at birth on the basis of this indicator, maternal age, singleton/multiple status, and claims for routine prenatal care tests. We assessed the accuracy of the algorithm-based estimates relative to the gold standard of the clinical gestational age at birth recorded in the delivery discharge record. RESULTS: The preterm-status indicator had specificity and sensitivity of 98% and 91%, respectively. Estimates from an algorithm that assigned 35 weeks of gestational age at birth to deliveries with the preterm-status indicator and 39 weeks to those without them were within 2 weeks of the clinical gestational age at birth in 75% of preterm and 99% of term deliveries. CONCLUSIONS: Subtracting 35 weeks (245 days) from the date of birth in deliveries with codes for preterm birth and 39 weeks (273 days) in those without them provided the optimal estimate of the beginning of pregnancy among the algorithms studied. Copyright © 2012 John Wiley & Sons, Ltd.

PMID: 22550030  [PubMed - as supplied by publisher]

LINK: http://dx.doi.org/10.1002/pds.3284

4. Cancer. 2012 Apr 19. doi: 10.1002/cncr.27552. [Epub ahead of print]

Data for cancer comparative effectiveness research: Past, present, and future potential. Meyer AM, Carpenter WR, Abernethy AP, Stürmer T, Kosorok MR. Universisty of North Carolina-Lineberger Comprehensive Cancer Center, University  of North Carolina, Chapel Hill, North Carolina; Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill, North Carolina.

Comparative effectiveness research (CER) can efficiently and rapidly generate new scientific evidence and address knowledge gaps, reduce clinical uncertainty, and guide health care choices. Much of the potential in CER is driven by the application of novel methods to analyze existing data. Despite its potential, several challenges must be identified and overcome so that CER may be improved, accelerated, and expeditiously implemented into the broad spectrum of cancer care and clinical practice. To identify and characterize the challenges to cancer CER, the authors reviewed the literature and conducted semistructured interviews with 41 cancer CER researchers at the Agency for Healthcare Research and Quality’s Developing Evidence to Inform Decisions about Effectiveness (DEcIDE) Cancer CER Consortium. Several data sets for cancer CER were identified and differentiated into an ontology of 8 categories and were characterized in terms of strengths, weaknesses, and utility. Several themes emerged during the development of this ontology and discussions with CER researchers. Dominant among them was accelerating cancer CER and promoting the acceptance of findings, which will necessitate transcending disciplinary silos to incorporate diverse perspectives and expertise. Multidisciplinary collaboration is required, including those with expertise in nonexperimental data, statistics, outcomes research, clinical trials, epidemiology, generalist and specialty medicine, survivorship, informatics, data, and methods, among others. Recommendations highlight the

systematic, collaborative identification of critical measures; application of more rigorous study design and sampling methods; policy-level resolution of issues in data ownership, governance, access, and cost; and development and application of consistent standards for data security, privacy, and confidentiality. Cancer 2012. © 2012 American Cancer Society.

PMID: 22517505  [PubMed - as supplied by publisher]

LINK: http://onlinelibrary.wiley.com/doi/10.1002/cncr.27552/abstract

5. Arch Intern Med. 2012 Apr 9;172(7):548-54.

Influenza vaccine effectiveness in patients on hemodialysis: an analysis of a natural experiment. McGrath LJ, Kshirsagar AV, Cole SR, Wang L, Weber DJ, Stürmer T, Brookhart MA. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 2105F McGavran-Greenberg, Campus Box CB 7435, Chapel Hill, NC 27599-7435. mabrook@email.unc.edu.

BACKGROUND: Although the influenza vaccine is recommended for patients with end-stage renal disease, little is known about its effectiveness. Observational studies of vaccine effectiveness (VE) are challenging because vaccinated subjects may be healthier than unvaccinated subjects.

METHODS: Using US Renal Data System data, we estimated VE for influenza-like illness, influenza/pneumonia hospitalization, and mortality in adult patients undergoing hemodialysis by using a natural experiment created by the year-to-year variation in the match of the influenza vaccine to the circulating virus. We compared vaccinated patients in matched years (1998, 1999, and 2001) with a mismatched year (1997) using Cox proportional hazards models. Ratios of hazard ratios compared vaccinated patients between 2 years and unvaccinated patients between 2 years. We calculated VE as 1 - effect measure.

RESULTS: Vaccination rates were less than 50% each year. Conventional analysis comparing vaccinated with unvaccinated patients produced average VE estimates of 13%, 16%, and 30% for influenza-like illness, influenza/pneumonia hospitalization, and mortality, respectively. When restricted to the preinfluenza period, results were even stronger, indicating bias. The pooled ratio of hazard ratios comparing matched seasons with a placebo season resulted in a VE of 0% (95% CI, -3% to 2%) for influenza-like illness, 2% (-2% to 5%) for hospitalization, and 0% (-3% to 3%) for death.

CONCLUSIONS: Relative to a mismatched year, we found little evidence of increased VE in subsequent well-matched years, suggesting that the current influenza vaccine strategy may have a smaller effect on morbidity and mortality in the end-stage renal disease population than previously thought. Alternate strategies (eg, high-dose vaccine, adjuvanted vaccine, and multiple doses) should be investigated.

PMID: 22493462  [PubMed - in process]

LINK: http://archinte.ama-assn.org/cgi/content/full/172/7/548

6. Epidemiology. 2012 Mar;23(2):223-32.

Using marginal structural models to estimate the direct effect of adverse childhood social conditions on onset of heart disease, diabetes, and stroke. Nandi A, Glymour MM, Kawachi I, VanderWeele TJ. Institute for Health and Social Policy and Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC Canada. arijit.nandi@mcgill.ca

Comment in Epidemiology. 2012 Mar;23(2):233-7.

BACKGROUND: Early-life socioeconomic status (SES) is associated with adult chronic disease, but it is unclear whether this effect is mediated entirely via adult SES or whether there is a direct effect of adverse early-life SES on adult disease. Major challenges in evaluating these alternatives include imprecise measurement of early-life SES and bias in conventional regression methods to assess mediation. In particular, conventional regression approaches to direct effect estimation are biased when there is time-varying confounding of the association between adult SES and chronic disease by chronic disease risk factors.

METHODS: First-reported heart disease, diabetes, and stroke diagnoses were assessed in a national sample of 9760 Health and Retirement Study participants followed biennially from 1992 through 2006. Early-life and adult SES measures were derived using exploratory and confirmatory factor analysis. Early-life SES was measured by parental education, father’s occupation, region of birth, and childhood rural residence. Adult SES was measured by respondent’s education, occupation, labor force status, household income, and household wealth. Using marginal structural models, we estimated the direct effect of early-life SES on chronic disease onset that was not mediated by adult SES. Marginal structural models were estimated with stabilized inverse probability-weighted log-linear models to adjust for risk factors that may have confounded associations between adult SES and chronic disease.

RESULTS: During follow-up, 24%, 18%, and 9% of participants experienced first onset of heart disease, diabetes, and stroke, respectively. Comparing those in the most disadvantaged with the least disadvantaged quartile, early-life SES was associated with coronary heart disease (risk ratio = 1.30 [95% confidence interval = 1.12-1.51]) and diabetes (1.23 [1.02-1.48]) and marginally associated  with stroke via pathways not mediated by adult SES.

CONCLUSIONS: Our results suggest that early-life socioeconomic experiences directly influence adult chronic disease outcomes.

PMID: 22317806  [PubMed - in process]

LINK: http://journals.lww.com/epidem/Abstract/2012/03000/Using_Marginal_Structural_Models_to_Estimate_the.8.aspx

MAY THEME: PDS proceedings from the 2011 DEcIDE Methods Symposium on methods for developing and analyzing clinically rich data for patient-centered outcomes

http://www.drugepi.org/recently-at-dope/journal-supplement-from-3rd-decide-now-available/

An open access supplement to Pharmacoepidemiology and Drug Safety’s released online on May 3, 2012 contains 17 papers presented at the third DEcIDE CER methods symposium, “Research Methods for Comparative Effectiveness (CER) and Patient-Centered [...]]]> Brigham DMC-sponsored Supplement on Methods for Developing and Analyzing Clinically Rich Data for Patient-Centered Outcomes Research is now published in PDS

An open access supplement to Pharmacoepidemiology and Drug Safety’s released online on May 3, 2012 contains 17 papers presented at the third DEcIDE CER methods symposium, “Research Methods for Comparative Effectiveness (CER) and Patient-Centered Outcomes Research (PCOR)” funded by the Agency for Health Care Research and Quality, through its Effective Health Care program. This supplement is the third published in recent years that have become important resources for investigators to use in designing and conducting comparative effectiveness research studies.

EDITORIAL

• Methods for developing and analyzing clinically rich data for patient-centered outcomes research. S. Schneeweiss, J. D. Seeger, and S. R. Smith

COMMENTARY

• When to randomize, or Evidence-Based Medicine needs Medicine-Based  Evidence. J. Concato

ORIGINAL ARTICLES
 Enriched Data Sources

• Analyzing partially missing confounder information in comparative effectiveness and safety research of therapeutics. S. Toh, L. A. García Rodríguez, and M. A. Hernán

• Identification of metastatic cancer in claims data. B. L. Nordstrom, J. L. Whyte, M. Stolar, C. Mercaldi, and J. D. Kallich

• Linkage of routinely collected oncology clinical data with health insurance claims data —an example with aromatase inhibitors, tamoxifen, and all-cause mortality. D.D. Dore, C. Liang, N. Ziyadeh, H. Norman, M. Bayliss, and J.D. Seeger

• Merging of NCI-funded cooperative oncology group data with an administrative data source to develop a more effective platform for clinical trial analysis and comparative effectiveness research: A report from the Children’s Oncology Group. R. Aplenc, B. T. Fisher, Y. S. Huang, Y. Li, T. A. Alonzo, R. B. Gerbing, M. Hall, D. Bertoch, R. Keren, A. E. Seif, L. Sung L, P. C. Adamson, A. Gamis

• Incorporating initial treatments improves performance of a mortality prediction model for patients with sepsis. T. Lagu, M. B. Rothberg, B. H. Nathanson, J. S. Steingrub, and P. K. Lindenauer

• Evaluation of total hip arthroplasty devices using a total joint replacement registry. E. W. Paxton, C. F. Ake, M. C. S. Inacio, M. Khatod, D. Marinac-Dabic, and A. Sedrakyan

 Improved Confounding Adjustment

• Prior Event Rate Ratio (PERR) adjustment: Numerical studies of a statistical methods to address unrecognized confounding in observational studies. M. Yu, D. Xie, X. Wang, M. G. Weiner, and R. L. Tannen

• One-to-many propensity score matching in cohort studies. J. A. Rassen, a. a. shelat, J. Myers, R.J. Glynn, K. J. Rothman, and S. Schneeweiss

• Performance of propensity score-based methods when exposed and comparison groups originate from different data sources. B. G. Hammill, L. H. Curtis, and S. Setoguchi

• Confronting ‘confounding by health system use’ in Medicare Part D: Comparative effectiveness of propensity score approaches to confounding adjustment. J. M. Polinski, S. Schneeweiss, R. J. Glynn, J. Lii, and J. A. Rassen

• Dynamic marginal structural modeling to evaluate the comparative effectiveness of more or less aggressive treatment intensification strategies in adults with type 2 diabetes. R. Neugebauer, B. Fireman, J. A. Roy, P. J. O’Connor, and J. V. Selby

• Simulation study of instrumental variable approaches with an application to a study of the antidiabetic effect of bezafibrate. B. Cai, S. Hennessy, J. H. Flory, D. Sha, T. R. Ten Have, and D. S. Small

 Heterogeneity of Treatment Effect

• Assessing the applicability of trial evidence to a target sample in the presence of heterogeneity of treatment effect. C. O. Weiss, J. B. Segal, and R. Varadhan

 Methods for Emerging Therapies

• Comparative effectiveness research using matching-adjusted indirect comparison: An application to treatment with guanfacine extended release or atomoxetine in children with ADHD and co-morbid oppositional defiant disorder. J. Signorovitch, M.H. Erder, J. Xie, V. Sikirica, M. Lu, P.S. Hodgkins, and E.Q. Wu

• Role of disease risk scores for comparative effectiveness with emerging therapies. R. J. Glynn, J. J. Gagne, and S. Schneeweiss

 Randomized Designs That Mimic Routine Care

• Reweighted mahalanobis distance matching for cluster-randomized trials with missing Data. R. A. Greevy, C. G. Grijalva, C. L. Roumie, C. Beck, A. M. Hung, H. J. Murff, X. Liu, and M. R. Griffin

NPR: Seniors In Medicare ‘Doughnut [...]]]> Jennifer Polinski, ScD, MPH, MS, and colleagues published a paper on the impact of the Medicare Part D Donut Hole on adherence to cardiovascular therapies. Her paper, published in Circulation: Cardiovascular Quality and Outcomes, is discussed by NPR and US News & World Report, among other media outlets.

NPR: Seniors In Medicare ‘Doughnut Hole’ More Likely To Stop Heart Drugs

US News & World Report: Seniors Stop Taking Heart Drugs In Medicare ‘Donut Hole’

NASDAQ GlobeNewswire: Patients Often Stop Taking Heart Drugs During Medicare Coverage Gaps

Abstract for the original article: Beneficiaries With Cardiovascular Disease and the Part D Coverage Gap

You can find them all here.

CER Scan [Epub ahead of print]

Testing superiority at interim analyses in a non-inferiority trial. [...]]]> The DEcIDE Methods Center publishes a monthly literature scan of current articles of interest to the field of comparative effectiveness research.

You can find them all here.

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]

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]

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]

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]

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]

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]

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]

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.

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]

Multiple Medications: Growing “polypharmacy” problem

You can find them all here.

CER Scan [Epub ahead of print]

Longitudinal structural mixed models for the analysis of surgical [...]]]> The DEcIDE Methods Center publishes a monthly literature scan of current articles of interest to the field of comparative effectiveness research.

You can find them all here.

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/

Chicago Tribune: Many Don’t Stick to Bone Drugs, Despite Counseling

You can find them all here.

CER Scan [Epub ahead of print]

Bias in Observational Studies of Prevalent Users: [...]]]> The DEcIDE Methods Center publishes a monthly literature scan of current articles of interest to the field of comparative effectiveness research.

You can find them all here.

February 2012

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

1. The U.S. Food and Drug Administration’s Mini-Sentinel program: status and direction (pages 1–8). Platt R, Carnahan RM, Brown JS, Chrischilles E, Curtis LH, Hennessy S, Nelson JC, Racoosin JA, Robb M, Schneeweiss S, Toh S, Weiner MG. Article first published online: 19 JAN 2012 | DOI: 10.1002/pds.2343

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.

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

2. A policy framework for public health uses of electronic health data (pages 18–22). McGraw D, Rosati K, Evans B. Article first published online: 19 JAN 2012 | DOI: 10.1002/pds.2319

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.

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

3. Design considerations, architecture, and use of the Mini-Sentinel distributed data system (pages 23–31). Curtis LH,Weiner MG, Boudreau DM, Cooper WO, Daniel GW, Nair VP, Raebel MA, Beaulieu NU, Rosofsky R, Woodworth TS, Brown JS. Article first published online: 19 JAN 2012 | DOI: 10.1002/pds.2336

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.

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

4. Using high-dimensional propensity scores to automate confounding control in a distributed medical product safety surveillance system (pages 41–49). Rassen JA, Schneeweiss S. Article first published online: 19 JAN 2012 | DOI: 10.1002/pds.2328

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.

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

5. When should case-only designs be used for safety monitoring of medical products? (pages 50–61). Maclure M, Fireman B, Nelson JC, Hua W, Shoaibi A, Paredes A, Madigan D. Article first published online: 19 JAN 2012 | DOI: 10.1002/pds.2330

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.

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

6. Challenges in the design and analysis of sequentially monitored postmarket safety surveillance evaluations using electronic observational health care data (pages 62–71). Nelson JC, Cook AJ, Yu O, Dominguez C, Zhao S, Greene SK, Fireman BH, Jacobsen SJ, Weintraub ES, Jackson LA. Article first published online: 19 JAN 2012 | DOI: 10.1002/pds.2324

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.

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

7. Statistical approaches to group sequential monitoring of postmarket safety surveillance data: current state of the art for use in the Mini-Sentinel pilot (pages 72–81). Cook AJ, Tiwari RC, Wellman RD, Heckbert SR, Li L, Heagerty P, Marsh T, Nelson JC. Article first published online: 19 JAN 2012 | DOI: 10.1002/pds.2320

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.

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

8. A protocol for active surveillance of acute myocardial infarction in association with the use of a new antidiabetic pharmaceutical agent (pages 282–290). Fireman B, Toh S, Butler MG, Go AS, Joffe HV, Graham DJ, Nelson JC, Daniel GW, Selby JV. Article first published online: 19 JAN 2012 | DOI: 10.1002/pds.2337

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.

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