  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.
