An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies.
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies.
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减少观察研究中混杂影响的倾向评分方法简介。
DOI：
10.1080/00273171.2011.568786
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发表时间：
2011-05
影响因子：
3.8
通讯作者：
Austin PC
中科院分区：
文献类型：
--
作者：
Austin PC
倾向评分是以观察到的基线特征为条件的治疗分配概率。倾向评分允许设计和分析观察性（非随机）研究，以便模仿随机对照试验的某些特定特征。特别是，倾向评分是一种平衡评分：在倾向评分的条件下，观察到的基线协变量的分布在治疗和未治疗受试者之间相似。我描述了4种不同的倾向评分方法：倾向评分匹配，倾向评分分层，使用倾向评分的治疗权重的逆概率，以及使用倾向评分的协变量调整。我描述的平衡诊断检查是否倾向得分模型已充分指定。此外，我还讨论了基于回归的方法和基于倾向得分的方法之间的差异，用于分析观测数据。我描述了不同的因果平均治疗效果及其与倾向评分分析的关系。
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.
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影响因子：
2
作者：
Austin, Peter C.
通讯作者：
Austin, Peter C.
影响因子：
2.6
作者：
Austin, Peter C.
通讯作者：
Austin, Peter C.
影响因子：
2
作者：
Austin, Peter C.
通讯作者：
Austin, Peter C.
影响因子：
2
作者：
Austin, Peter C.
通讯作者：
Austin, Peter C.
影响因子：
2
作者：
Austin, PC;Mamdani, MM
通讯作者：
Mamdani, MM