Global sensitivity analysis for repeated measures studies with informative drop-out: A semi-parametric approach

Daniel Scharfstein, Aidan McDermott, Iván Díaz, Marco Carone, Nicola Lunardon, Ibrahim Turkoz

Research output: Contribution to journalArticlepeer-review

Abstract

In practice, both testable and untestable assumptions are generally required to draw inference about the mean outcome measured at the final scheduled visit in a repeated measures study with drop-out. Scharfstein et al. (2014) proposed a sensitivity analysis methodology to determine the robustness of conclusions within a class of untestable assumptions. In their approach, the untestable and testable assumptions were guaranteed to be compatible; their testable assumptions were based on a fully parametric model for the distribution of the observable data. While convenient, these parametric assumptions have proven especially restrictive in empirical research. Here, we relax their distributional assumptions and provide a more flexible, semi-parametric approach. We illustrate our proposal in the context of a randomized trial for evaluating a treatment of schizoaffective disorder.

Original languageEnglish (US)
Pages (from-to)207-219
Number of pages13
JournalBiometrics
Volume74
Issue number1
DOIs
StatePublished - Mar 2018

Keywords

  • Bootstrap
  • Cross-validation
  • Exponential tilting
  • Identifiability
  • Jackknife
  • One-step estimator
  • Plug-in estimator
  • Selection bias

ASJC Scopus subject areas

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

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