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Abstract

Estimation of the effect of a treatment or exposure with a causal interpretation from studies where exposure is not randomized may be biased if confounding and selection bias are not taken into appropriate account. Such adjustment for confounding is often carried out through regression modeling of the relationships among treatment, confounders, and outcome. Correct specification of the regression m odel is one of the most fundamental assumptions in statistical analysis. Even when all relevant confounders have been measured, an unbiased estimate of the treatment effect will be obtained only if the model itself reflects the true relationship among treatment, confounders, and the outcome. Outside of simulation studies, we can never know whether or not the model we have constructed includes all relevant confounders and accurately depicts those relationships. Doubly robust estimation of the effect of exposure on outcome combines inverse probability weighting by a propensity score with regression modeling in such a way that as long as either the propensity score model is correctly specified or the regression model is correctly specified the effect of the exposure on the outcome will be correctly estimated, assuming that there are no unmeasured confounders. While several authors have shown doubly-robust estimators to be powerful tools for modeling, they are not in common usage yet in part because they are difficult to implement. We have developed a simple SAS® macro for obtaining doubly robust estimates. We will present sample code and results from analyses of simulated data.