Global sensitivity analysis of randomized trials with nonmonotone missing binary outcomes: Application to studies of substance use disorders

In this paper, we present a method for conducting global sensitivity analysis of randomized trials in which binary outcomes are scheduled to be collected on participants at pre-specified points in time after randomization and these outcomes may be missing in a non-monotone fashion. We introduce a class of missing data assumptions, indexed by sensitivity parameters, that are anchored around the missing not at random assumption introduced by Robins (Statistics in Medicine, 1997). For each assumption in the class, we establish that the joint distribution of the outcomes are identifiable from the distribution of the observed data. Our estimation procedure uses the plug-in principle, where the distribution of the observed data is estimated using random forests. We establish √n asymptotic properties for our estimation procedure. We illustrate our methodology in the context of a randomized trial designed to evaluate a new approach to reducing substance use, assessed by testing urine samples twice weekly, among patients entering outpatient addiction treatment. We evaluate the finite sample properties of our method in a realistic simulation study. Our methods have been implemented in an R package entitled slabm. This article is protected by copyright. All rights reserved.