D'Agostino R B
Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27157-1063, USA.
Stat Med. 1998 Oct 15;17(19):2265-81. doi: 10.1002/(sici)1097-0258(19981015)17:19<2265::aid-sim918>3.0.co;2-b.
In observational studies, investigators have no control over the treatment assignment. The treated and non-treated (that is, control) groups may have large differences on their observed covariates, and these differences can lead to biased estimates of treatment effects. Even traditional covariance analysis adjustments may be inadequate to eliminate this bias. The propensity score, defined as the conditional probability of being treated given the covariates, can be used to balance the covariates in the two groups, and therefore reduce this bias. In order to estimate the propensity score, one must model the distribution of the treatment indicator variable given the observed covariates. Once estimated the propensity score can be used to reduce bias through matching, stratification (subclassification), regression adjustment, or some combination of all three. In this tutorial we discuss the uses of propensity score methods for bias reduction, give references to the literature and illustrate the uses through applied examples.
在观察性研究中，研究者无法控制治疗分配。接受治疗组和未接受治疗组（即对照组）在观察到的协变量上可能存在很大差异，而这些差异可能导致治疗效果的估计出现偏差。即使是传统的协方差分析调整也可能不足以消除这种偏差。倾向得分定义为给定协变量时接受治疗的条件概率，可用于平衡两组中的协变量，从而减少这种偏差。为了估计倾向得分，必须对给定观察到的协变量时治疗指标变量的分布进行建模。一旦估计出倾向得分，就可以通过匹配、分层（亚分类）、回归调整或这三种方法的某种组合来减少偏差。在本教程中，我们讨论倾向得分方法在减少偏差方面的用途，提供文献参考，并通过应用示例说明其用途。
