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Propensity score methods for bias reduction in the comparison of a treatment to a non‐randomized control group
Author(s) -
D'Agostino Ralph B.
Publication year - 1998
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19981015)17:19<2265::aid-sim918>3.0.co;2-b
Subject(s) - propensity score matching , covariate , statistics , observational study , average treatment effect , analysis of covariance , matching (statistics) , econometrics , treatment and control groups , mathematics
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.