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      Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies


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          The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher‐order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

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          Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men.

          Standard methods for survival analysis, such as the time-dependent Cox model, may produce biased effect estimates when there exist time-dependent confounders that are themselves affected by previous treatment or exposure. Marginal structural models are a new class of causal models the parameters of which are estimated through inverse-probability-of-treatment weighting; these models allow for appropriate adjustment for confounding. We describe the marginal structural Cox proportional hazards model and use it to estimate the causal effect of zidovudine on the survival of human immunodeficiency virus-positive men participating in the Multicenter AIDS Cohort Study. In this study, CD4 lymphocyte count is both a time-dependent confounder of the causal effect of zidovudine on survival and is affected by past zidovudine treatment. The crude mortality rate ratio (95% confidence interval) for zidovudine was 3.6 (3.0-4.3), which reflects the presence of confounding. After controlling for baseline CD4 count and other baseline covariates using standard methods, the mortality rate ratio decreased to 2.3 (1.9-2.8). Using a marginal structural Cox model to control further for time-dependent confounding due to CD4 count and other time-dependent covariates, the mortality rate ratio was 0.7 (95% conservative confidence interval = 0.6-1.0). We compare marginal structural models with previously proposed causal methods.
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            A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003.

            Propensity-score methods are increasingly being used to reduce the impact of treatment-selection bias in the estimation of treatment effects using observational data. Commonly used propensity-score methods include covariate adjustment using the propensity score, stratification on the propensity score, and propensity-score matching. Empirical and theoretical research has demonstrated that matching on the propensity score eliminates a greater proportion of baseline differences between treated and untreated subjects than does stratification on the propensity score. However, the analysis of propensity-score-matched samples requires statistical methods appropriate for matched-pairs data. We critically evaluated 47 articles that were published between 1996 and 2003 in the medical literature and that employed propensity-score matching. We found that only two of the articles reported the balance of baseline characteristics between treated and untreated subjects in the matched sample and used correct statistical methods to assess the degree of imbalance. Thirteen (28 per cent) of the articles explicitly used statistical methods appropriate for the analysis of matched data when estimating the treatment effect and its statistical significance. Common errors included using the log-rank test to compare Kaplan-Meier survival curves in the matched sample, using Cox regression, logistic regression, chi-squared tests, t-tests, and Wilcoxon rank sum tests in the matched sample, thereby failing to account for the matched nature of the data. We provide guidelines for the analysis and reporting of studies that employ propensity-score matching. Copyright (c) 2007 John Wiley & Sons, Ltd.
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              Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study.

              Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting for confounding: methods based on stratification of observations by quantiles of estimated propensity scores and methods based on weighting observations by the inverse of estimated propensity scores. We review popular versions of these approaches and related methods offering improved precision, describe theoretical properties and highlight their implications for practice, and present extensive comparisons of performance that provide guidance for practical use. 2004 John Wiley & Sons, Ltd.

                Author and article information

                Stat Med
                Stat Med
                Statistics in Medicine
                John Wiley and Sons Inc. (Hoboken )
                03 August 2015
                10 December 2015
                : 34
                : 28 ( doiID: 10.1002/sim.v34.28 )
                : 3661-3679
                [ 1 ]Institute for Clinical Evaluative Sciences Toronto OntarioCanada
                [ 2 ] Institute of Health Policy, Management and EvaluationUniversity of Toronto TorontoCanada
                [ 3 ] Schulich Heart Research ProgramSunnybrook Research Institute TorontoCanada
                [ 4 ] Department of Mental HealthJohns Hopkins Bloomberg School of Public Health Baltimore MDU.S.A.
                [ 5 ] Department of BiostatisticsJohns Hopkins Bloomberg School of Public Health Baltimore MDU.S.A.
                [ 6 ] Department of Health Policy and ManagementJohns Hopkins Bloomberg School of Public Health Baltimore MDU.S.A.
                Author notes
                [*] [* ] Correspondence to: Peter C. Austin, Institute for Clinical Evaluative Sciences, G106, 2075 Bayview Avenue, Toronto, Ontario, M4N 3M5, Canada.

                E‐mail: peter.austin@ 123456ices.on.ca

                SIM6607 sim.6607
                © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                : 08 April 2015
                : 16 June 2015
                : 09 July 2015
                Page count
                Pages: 19
                Funded by: (ICES)
                Funded by: (MOHLTC)
                Funded by: (CIHR)
                Award ID: 86508
                Award ID: R01MH099010
                Funded by: (ICES)
                Research Article
                Research Articles
                Custom metadata
                10 December 2015
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.9.4 mode:remove_FC converted:09.09.2016

                observational study,propensity score,inverse probability of treatment weighting,iptw,causal inference


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