Global Sensitivity Analysis for Repeated Measures Studies With Informative Dropout: A Fully Parametric Approach

Daniel Scharfstein, Aidan McDermott, William Olson, Frank Wiegand

Research output: Contribution to journalArticlepeer-review

Abstract

We present a global sensitivity analysis methodology for drawing inference about the mean at the final scheduled visit in a repeated measures study with informative dropout. We review and critique the sensitivity frameworks developed by Rotnitzky et al. and Daniels and Hogan. We identify strengths and weaknesses of these approaches and propose an alternative. We illustrate our approach via a comprehensive analysis of the RIS-INT-3 trial.

Original languageEnglish (US)
Pages (from-to)338-348
Number of pages11
JournalStatistics in Biopharmaceutical Research
Volume6
Issue number4
DOIs
StatePublished - Oct 2 2014

Keywords

  • Curse of dimensionality
  • Explainable dropout
  • Exponential tilting
  • G-computation
  • Identification
  • Missing at random
  • Pattern-mixture model
  • Selection model

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmaceutical Science

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