On regression adjustment for the propensity score.
Propensity scores are widely adopted in observational research because they enable adjustment for high-dimensional confounders without requiring models for their association with the outcome of interest. The results of statistical analyses based on stratification, matching or inverse weighting by the propensity score are therefore less susceptible to model extrapolation than those based solely on outcome regression models. This is attractive because extrapolation in outcome regression models may be alarming, yet difficult to diagnose, when the exposed and unexposed individuals have very different covariate distributions. Standard regression adjustment for the propensity score forms an alternative to the aforementioned propensity score methods, but the benefits of this are less clear because it still involves modelling the outcome in addition to the propensity score. In this article, we develop novel insights into the properties of this adjustment method. We demonstrate that standard tests of the null hypothesis of no exposure effect (based on robust variance estimators), as well as particular standardised effects obtained from such adjusted regression models, are robust against misspecification of the outcome model when a propensity score model is correctly specified; they are thus not vulnerable to the aforementioned problem of extrapolation. We moreover propose efficient estimators for these standardised effects, which retain a useful causal interpretation even when the propensity score model is misspecified, provided the outcome regression model is correctly specified.
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium.
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Vansteelandt
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Daniel
D000328:Adult; D006328:Cardiac Catheterization / Q000401:mortality / Q000706:statistics & numerical data; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000706:statistics & numerical data; D057216:Propensity Score; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*
causal inference; effect measure; model misspecification; pooling; positivity; propensity score; standardisation; strong confounding