Austin Peter C
Institute for Clinical Evaluative Sciences Department of Health Management, Policy and Evaluation, University of Toronto.
Multivariate Behav Res. 2011 May;46(3):399-424. doi: 10.1080/00273171.2011.568786. Epub 2011 Jun 8.
The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
倾向得分是基于观察到的基线特征进行治疗分配的概率。倾向得分使人们能够设计和分析一项观察性（非随机）研究，使其模仿随机对照试验的一些特定特征。具体而言，倾向得分是一个平衡得分：在倾向得分的条件下，治疗组和未治疗组受试者观察到的基线协变量分布将相似。我描述了4种不同的倾向得分方法：基于倾向得分进行匹配、基于倾向得分进行分层、使用倾向得分进行治疗加权的逆概率法以及使用倾向得分进行协变量调整。我描述了用于检查倾向得分模型是否已得到充分设定的平衡诊断方法。此外，我讨论了基于回归的方法和基于倾向得分的方法在观察性数据分析中的差异。我描述了不同的因果平均治疗效果及其与倾向得分分析的关系。
