Doubly Robust Estimation with the R Package drgee

Abstract A common goal of epidemiologic research is to study the association between a certain exposure and a certain outcome, while controlling for important covariates. This is often done by fitting a restricted mean model for the outcome, as in generalized linear models (GLMs) and in generalized estimating equations (GEEs). If the covariates are high-dimensional, then it may be difficult to well specify the model. This is an important concern, since model misspecification may lead to biased estimates. Doubly robust estimation is an estimation technique that offers some protection against model misspecification. It utilizes two models, one for the outcome and one for the exposure, and produces unbiased estimates of the exposure-outcome association if either model is correct, not necessarily both. Despite its obvious appeal, doubly robust estimation is not used on a regular basis in applied epidemiologic research. One reason for this could be the lack of up-to-date software. In this paper we describe a new R package, drgee , which carries out doubly robust estimation in restricted mean models. The package is constructed to be user-friendly and fast, to facilitate routine use of doubly robust estimation. The paper is structured into theory sections and example sections. The former are intended to serve as a brief but self-consistent tutorial in doubly robust estimation. The latter illustrate the use of the drgee package through practical examples. We have used publically available data throughout the paper, so that the reader can easily replicate all examples.