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      Propensity Score Matching: A Conceptual Review for Radiology Researchers

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          Abstract

          The propensity score is defined as the probability of each individual study subject being assigned to a group of interest for comparison purposes. Propensity score adjustment is a method of ensuring an even distribution of confounders between groups, thereby increasing between group comparability. Propensity score analysis is therefore an increasingly applied statistical method in observational studies. The purpose of this article was to provide a step-by-step nonmathematical conceptual guide to propensity score analysis with particular emphasis on propensity score matching. A software program code used for propensity score matching was also presented.

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          Most cited references 38

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          A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003.

           P. Austin (2008)
          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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            A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study.

            The propensity score--the probability of exposure to a specific treatment conditional on observed variables--is increasingly being used in observational studies. Creating strata in which subjects are matched on the propensity score allows one to balance measured variables between treated and untreated subjects. There is an ongoing controversy in the literature as to which variables to include in the propensity score model. Some advocate including those variables that predict treatment assignment, while others suggest including all variables potentially related to the outcome, and still others advocate including only variables that are associated with both treatment and outcome. We provide a case study of the association between drug exposure and mortality to show that including a variable that is related to treatment, but not outcome, does not improve balance and reduces the number of matched pairs available for analysis. In order to investigate this issue more comprehensively, we conducted a series of Monte Carlo simulations of the performance of propensity score models that contained variables related to treatment allocation, or variables that were confounders for the treatment-outcome pair, or variables related to outcome or all variables related to either outcome or treatment or neither. We compared the use of these different propensity scores models in matching and stratification in terms of the extent to which they balanced variables. We demonstrated that all propensity scores models balanced measured confounders between treated and untreated subjects in a propensity-score matched sample. However, including only the true confounders or the variables predictive of the outcome in the propensity score model resulted in a substantially larger number of matched pairs than did using the treatment-allocation model. Stratifying on the quintiles of any propensity score model resulted in residual imbalance between treated and untreated subjects in the upper and lower quintiles. Greater balance between treated and untreated subjects was obtained after matching on the propensity score than after stratifying on the quintiles of the propensity score. When a confounding variable was omitted from any of the propensity score models, then matching or stratifying on the propensity score resulted in residual imbalance in prognostically important variables between treated and untreated subjects. We considered four propensity score models for estimating treatment effects: the model that included only true confounders; the model that included all variables associated with the outcome; the model that included all measured variables; and the model that included all variables associated with treatment selection. Reduction in bias when estimating a null treatment effect was equivalent for all four propensity score models when propensity score matching was used. Reduction in bias was marginally greater for the first two propensity score models than for the last two propensity score models when stratification on the quintiles of the propensity score model was employed. Furthermore, omitting a confounding variable from the propensity score model resulted in biased estimation of the treatment effect. Finally, the mean squared error for estimating a null treatment effect was lower when either of the first two propensity scores was used compared to when either of the last two propensity score models was used. Copyright 2006 John Wiley & Sons, Ltd.
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              Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect.

              Observational studies often provide the only available information about treatment effects. Control of confounding, however, remains challenging. The authors compared five methods for evaluating the effect of tissue plasminogen activator on death among 6,269 ischemic stroke patients registered in a German stroke registry: multivariable logistic regression, propensity score-matched analysis, regression adjustment with the propensity score, and two propensity score-based weighted methods-one estimating the treatment effect in the entire study population (inverse-probability-of-treatment weights), another in the treated population (standardized-mortality-ratio weights). Between 2000 and 2001, 212 patients received tissue plasminogen activator. The crude odds ratio between tissue plasminogen activator and death was 3.35 (95% confidence interval: 2.28, 4.91). The adjusted odds ratio depended strongly on the adjustment method, ranging from 1.11 (95% confidence interval: 0.67, 1.84) for the standardized-mortality-ratio weighted to 10.77 (95% confidence interval: 2.47, 47.04) for the inverse-probability-of-treatment-weighted analysis. For treated patients with a low propensity score, risks of dying were high. Exclusion of patients with a propensity score of <5% yielded comparable odds ratios of approximately 1 for all methods. High levels of nonuniform treatment effect render summary estimates very sensitive to the weighting system explicit or implicit in an adjustment technique. Researchers need to be clear about the population for which an overall treatment estimate is most suitable.
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                Author and article information

                Journal
                Korean J Radiol
                Korean J Radiol
                KJR
                Korean Journal of Radiology
                The Korean Society of Radiology
                1229-6929
                2005-8330
                Mar-Apr 2015
                27 February 2015
                : 16
                : 2
                : 286-296
                Affiliations
                [1 ]Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, Seoul 138-736, Korea.
                [2 ]Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 138-736, Korea.
                [3 ]Department of Radiology, NYU Langone Medical Center, New York, NY 10016, USA.
                [4 ]Office of Clinical Research Information, Asan Medical Center, Seoul 138-736, Korea.
                [5 ]Department of Preventive Medicine, University of Ulsan College of Medicine, Seoul 138-736, Korea.
                Author notes
                Corresponding author: Hwa Jung Kim, MD, PhD, Departments of Preventive Medicine and Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 138-736, Korea. Tel: (822) 3010-5636, Fax: (822) 477-2898, hello.hello.hj@ 123456gmail.com
                Article
                10.3348/kjr.2015.16.2.286
                4347264
                Copyright © 2015 The Korean Society of Radiology

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Categories
                Experimental and Others
                Review Article

                Radiology & Imaging

                propensity score, matching, observational study, indication bias

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