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.

March 2011


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,
    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]

    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.
    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. 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.
    PMID: 21233141 [PubMed – as supplied by publisher]

    8. 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]
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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]

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