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Scharfstein et al. (2021) developed a sensitivity analysis model foranalyzing randomized trials with repeatedly measured binary outcomes that aresubject to nonmonotone missingness. Their approach becomes computationallyintractable when the number of repeated measured is large (e.g., greater than15). In this paper, we repair this problem by introducing an $m$th-orderMarkovian restriction. We establish an identification by representing the modelas a directed acyclic graph (DAG). We illustrate our methodology in the contextof a randomized trial designed to evaluate a web-delivered psychosocialintervention to reduce substance use, assessed by testing urine samples twiceweekly for 12 weeks, among patients entering outpatient addiction treatment. Weevaluate the finite sample properties of our method in a realistic simulationstudy. Our methods have been integrated into the R package entitled slabm.

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