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Balanced Subclassification in Observational Studies Using the Propensity Score: A Case Study.

机译:使用倾向得分进行观察研究中的平衡子分类:案例研究。

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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 theeoretical arguments have shown that subclassification on the scalar propensity score will balance all observed covariates. The procedure is illustrated in a large observational study of treatments for coronary artery disease. Five subclasses are constructed that balance 74 covariates. Balanced subclassification is combined with model-based adjustments to provide estimates of treatment effects within subpopulations. Two appendices address theoretical issues: propensity scores from incomplete data, and the effectiveness of subclassification on the propensity score.

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