Methods for conducting sensitivity analysis of trials with potentially nonignorable competing causes of censoring

Andrea Rotnitzky, Daniel Scharfstein, Ting Li Su, James Robins

Research output: Contribution to journalArticle

Abstract

We consider inference for the treatment-arm mean difference of an outcome that would have been measured at the end of a randomized follow-up study if, during the course of the study, patients had not initiated a nonrandomized therapy or dropped out. We argue that the treatment-arm mean difference is not identified unless unverifiable assumptions are made. We describe identifying assumptions that are tantamount to postulating relationships between the components of a pattern-mixture model but that can also be interpreted as imposing restrictions on the cause-specific censoring probabilities of a selection model. We then argue that, although sufficient for identification, these assumptions are insufficient for inference due to the curse of dimensionality. We propose reducing dimensionality by specifying semiparametric cause-specific selection models. These models are useful for conducting a sensitivity analysis to examine how inference for the treatment-arm mean difference changes as one varies the magnitude of the cause-specific selection bias over a plausible range. We provide methodology for conducting such sensitivity analysis and illustrate our methods with an analysis of data from the AIDS Clinical Trial Group (ACTG) study 002.

Original languageEnglish (US)
Pages (from-to)103-113
Number of pages11
JournalBiometrics
Volume57
Issue number1
DOIs
StatePublished - 2001

Keywords

  • Attrition
  • Augmented inverse probability of censoring weighted estimation
  • Curse of dimensionality
  • Longitudinal data
  • Noncompliance
  • Pattern-mixture models
  • Selection bias
  • Selection models

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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