Global sensitivity analysis of randomized trials with nonmonotone missing binary outcomes: Application to studies of substance use disorders

Daniel O. Scharfstein, Jon Steingrimsson, Aidan McDermott, Chenguang Wang, Souvik Ray, Aimee Campbell, Edward Nunes, Abigail Matthews

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we present a method for conducting global sensitivity analysis of randomized trials in which binary outcomes are scheduled to be collected on participants at prespecified points in time after randomization and these outcomes may be missing in a nonmonotone fashion. We introduce a class of missing data assumptions, indexed by sensitivity parameters, which 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 is identifiable 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 (Formula presented.) 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. We evaluate the finite sample properties of our method in a realistic simulation study. Our methods have been implemented in an R package entitled slabm.

Original languageEnglish (US)
Pages (from-to)649-659
Number of pages11
JournalBiometrics
Volume78
Issue number2
DOIs
StatePublished - Jun 2022

Keywords

  • exponential tilting
  • missing not at random
  • random forests

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Applied Mathematics
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Statistics and Probability

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