xtgeebcv: A command for bias-corrected sandwich variance estimation for GEE analyses of cluster randomized trials

John A. Gallis (), Fan Li () and Elizabeth L. Turner ()
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John A. Gallis: Duke University
Fan Li: Yale School of Public Health
Elizabeth L. Turner: Duke University

Stata Journal, 2020, vol. 20, issue 2, 363-381

Abstract: Cluster randomized trials, where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on indi- viduals, are commonly used to evaluate interventions in public health, education, and the social sciences. Analysis is often conducted on individual-level outcomes, and such analysis methods must consider that outcomes for members of the same cluster tend to be more similar than outcomes for members of other clusters. A popular individual-level analysis technique is generalized estimating equations (GEE). However, it is common to randomize a small number of clusters (for ex- ample, 30 or fewer), and in this case, the GEE standard errors obtained from the sandwich variance estimator will be biased, leading to inflated type I errors. Some bias-corrected standard errors have been proposed and studied to account for this finite-sample bias, but none has yet been implemented in Stata. In this article, we describe several popular bias corrections to the robust sandwich variance. We then introduce our newly created command, xtgeebcv, which will allow Stata users to easily apply finite-sample corrections to standard errors obtained from GEE mod- els. We then provide examples to demonstrate the use of xtgeebcv. Finally, we discuss suggestions about which finite-sample corrections to use in which situations and consider areas of future research that may improve xtgeebcv. Copyright 2020 by StataCorp LP.

Keywords: xtgeebcv; cluster randomized trials; bias-corrected variances; sandwich variance; generalized estimating equations; finite-sample correction (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1177/1536867X20931001

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