Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group

TLDR

Observational studies lack control over treatment assignment, and differences in covariates between treated and control groups can bias treatment effect estimates, a bias that traditional covariance adjustments may not fully eliminate. The tutorial aims to explain how propensity score methods reduce bias, provide literature references, and illustrate their application with examples. Propensity scores are estimated by modeling the probability of treatment given covariates and then used to reduce bias through matching, stratification, regression adjustment, or combinations thereof. © 1998 John Wiley & Sons, Ltd.

Abstract

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. © 1998 John Wiley & Sons, Ltd.