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      Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study.

      Statistics in Medicine

      Treatment Outcome, Regression Analysis, Monte Carlo Method, Humans, Data Interpretation, Statistical, Computer Simulation

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          Abstract

          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.

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          Journal
          10.1002/sim.1903
          15351954

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