Markov-Restricted Analysis of Randomized Trials with Non-Monotone Missing Binary Outcomes: Sensitivity Analysis and Identification Results

Scharfstein et al. (2021) developed a sensitivity analysis model for analyzing randomized trials with repeatedly measured binary outcomes that are subject to nonmonotone missingness. Their approach becomes computationally intractable when the number of repeated measured is large (e.g., greater than 15). In this paper, we repair this problem by introducing an $m$th-order Markovian restriction. We establish an identification by representing the model as a directed acyclic graph (DAG). We illustrate our methodology in the context of a randomized trial designed to evaluate a web-delivered psychosocial intervention to reduce substance use, assessed by testing urine samples twice weekly for 12 weeks, among patients entering outpatient addiction treatment. We evaluate the finite sample properties of our method in a realistic simulation study. Our methods have been integrated into the R package entitled slabm.