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Standardization-a method used to adjust for confounding-estimates counterfactual risks in a target population. To adjust for confounding variables that contain too many combinations to be fully stratified, two model-based standardization methods exist: regression standardization and use of an inverse probability of exposure weighted-reweighted estimators. Whereas the former requires an outcome regression model conditional on exposure and confounders, the latter requires a propensity score model. In reconciling among their modeling assumptions, doubly robust estimators, which only require correct specification of either the outcome regression or the propensity score model but do not necessitate both, have been well studied for total populations. Here, we provide doubly robust estimators of standardized risk difference and ratio in the exposed population. Theoretical details, simple model extension for independently censored outcomes, and a SAS program are provided in the eAppendix (


Tomohiro Shinozaki, Yutaka Matsuyama. Brief Report: Doubly Robust Estimation of Standardized Risk Difference and Ratio in the Exposed Population. Epidemiology (Cambridge, Mass.). 2015 Nov;26(6):873-7

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PMID: 26275176

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