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arXiv · 1910.11397

The covariate-adjusted residual estimator and its use in both randomized trials and observational settings

Stephen A. Lauer·Nicholas G. Reich·Laura B. Balzer
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Abstract

We often seek to estimate the causal effect of an exposure on a particular outcome in both randomized and observational settings. One such estimation method is the covariate-adjusted residuals estimator, which was designed for individually or cluster randomized trials. In this manuscript, we study the properties of this estimator and develop a new estimator that utilizes both covariate adjustment and inverse probability weighting We support our theoretical results with a simulation study and an application in an infectious disease setting. The covariate-adjusted residuals estimator is an efficient and unbiased estimator of the average treatment effect in randomized trials; however, it is not guaranteed to be unbiased in observational studies. Our novel estimator, the covariate-adjusted residuals estimator with inverse probability weighting, is unbiased in randomized and observational settings, under a reasonable set of assumptions. Furthermore, when these assumptions hold, it provides efficiency gains over inverse probability weighting in observational studies. The covariate-adjusted residuals estimator is valid for use in randomized trials, but should not be used in observational studies. The covariate-adjusted residuals estimator with inverse probability weighting provides an efficient alternative for use in randomized and observational settings.

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BibTeXRIS

Stephen A. Lauer, Nicholas G. Reich, Laura B. Balzer. 2019-10-24. The covariate-adjusted residual estimator and its use in both randomized trials and observational settings. https://arxiv.org/abs/1910.11397

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