Bristol Heart Institute, University of Bristol, School of Clinical Sciences, Bristol, UK.
Department of Cardiothoracic Surgery, Erasmus Medical Center, Rotterdam, Netherlands.
Eur J Cardiothorac Surg. 2018 Jun 1;53(6):1112-1117. doi: 10.1093/ejcts/ezy167.
Propensity score (PS) methods offer certain advantages over more traditional regression methods to control for confounding by indication in observational studies. Although multivariable regression models adjust for confounders by modelling the relationship between covariates and outcome, the PS methods estimate the treatment effect by modelling the relationship between confounders and treatment assignment. Therefore, methods based on the PS are not limited by the number of events, and their use may be warranted when the number of confounders is large, or the number of outcomes is small. The PS is the probability for a subject to receive a treatment conditional on a set of baseline characteristics (confounders). The PS is commonly estimated using logistic regression, and it is used to match patients with similar distribution of confounders so that difference in outcomes gives unbiased estimate of treatment effect. This review summarizes basic concepts of the PS matching and provides guidance in implementing matching and other methods based on the PS, such as stratification, weighting and covariate adjustment.
倾向评分（PS）方法在观察性研究中通过控制指示性混杂因素，提供了比传统回归方法的某些优势。虽然多变量回归模型通过建立协变量与结局之间的关系来调整混杂因素，但 PS 方法通过建立混杂因素与治疗分配之间的关系来估计治疗效果。因此，基于 PS 的方法不受事件数量的限制，当混杂因素数量较多或结局数量较少时，其使用可能是合理的。PS 是指在一组基线特征（混杂因素）条件下，个体接受治疗的概率。PS 通常使用逻辑回归进行估计，并用于匹配混杂因素分布相似的患者，以便结局的差异能提供治疗效果的无偏估计。本综述总结了 PS 匹配的基本概念，并提供了基于 PS 的匹配和其他方法（如分层、加权和协变量调整）的实施指导。
