Markov-Restricted Analysis of Randomized Trials with Non-Monotone Missing Binary Outcomes: Sensitivity Analysis and Identification Results
Tuesday, Aug 5: 9:35 AM - 9:55 AM
Topic-Contributed Paper Session
Music City Center
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 measurements is large (e.g., greater than 15). In this paper, we repair this problem by introducing mth-order Markovian restrictions. We establish identification results 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. This work is joint with Jaron Lee, Ilya Shpitser, Agatha Mallett, Aimee Campbell and Edward Nunes.
non-monotone missingness
Markov-restricted analysis
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