Reducing Bias in Observational Studies Using Subclassification on the Propensity Score

来自 Taylor & Francis

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阅读量：

499

作者：

PR Rosenbaum，DB Rubin

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摘要：

The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Previous theoretical arguments have shown that subclassification on the propensity score will balance all observed covariates. Subclassification on an estimated propensity score is illustrated, using observational data on treatments for coronary artery disease. Five subclasses defined by the estimated propensity score are constructed that balance 74 covariates, and thereby provide estimates of treatment effects using direct adjustment. These subclasses are applied within sub-populations, and model-based adjustments are then used to provide estimates of treatment effects within these sub-populations. Two appendixes address theoretical issues related to the application: the effectiveness of subclassification on the propensity score in removing bias, and balancing properties of propensity scores with incomplete data.

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关键词：

Bias reduction Stratification Logistic models Log-linear models Direct adjustment Balancing scores

DOI：

10.1080/01621459.1984.10478078

被引量：

6914

年份：

1984