Diagnosing Covariate Balance Across Levels of Right-Censoring, Before and After Applying Inverse Probability of Censoring Weights.

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Am J Epidemiol 2019 May 30. Epub 2019 May 30.

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health.

Covariate balance is a central concept in the potential outcomes literature. With selected populations or missing data, balance across treatment groups can be insufficient for estimating marginal treatment effects. Recently, a framework for using covariate balance to describe measured confounding and selection-bias for time-varying and other multivariate exposures in the presence of right-censoring has been proposed. Here, we revisit this framework to consider balance across levels of right-censoring over time in more depth. Specifically, we develop measures of covariate balance that can describe what is known as 'dependent censoring' in the literature, along with its associated selection-bias, under multiple mechanisms for right censoring. Such measures are interesting because they substantively describe the evolution of dependent censoring mechanism(s). Furthermore, we provide weighted versions that can depict how well such dependent censoring has been eliminated when inverse probability of censoring weights are applied. These results provide a conceptually grounded way to inspect covariate balance across levels of right-censoring as a validity check. As a motivating example, we apply these measures to a study of hypothetical 'static' and 'dynamic' treatment protocols in a sequential multiple assignment randomized trial of antipsychotics with high dropout rates.

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http://dx.doi.org/10.1093/aje/kwz136DOI Listing
May 2019
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