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Abstract:
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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 identiable 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 root-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. This work is joint with Jon Steingrimsson, Aidan McDermott, Chenguang Wang, Souvik Ray, Aimee Campbell, Edward Nunes and Abigail Matthews.
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