Global sensitivity analysis of clinical trials with missing patient-reported outcomes

Research output: Contribution to journalArticle

Abstract

Randomized trials with patient-reported outcomes are commonly plagued by missing data. The analysis of such trials relies on untestable assumptions about the missing data mechanism. To address this issue, it has been recommended that the sensitivity of the trial results to assumptions should be a mandatory reporting requirement. In this paper, we discuss a recently developed methodology (Scharfstein et al., Biometrics, 2018) for conducting sensitivity analysis of randomized trials in which outcomes are scheduled to be measured at fixed points in time after randomization and some subjects prematurely withdraw from study participation. The methodology is explicated in the context of a placebo-controlled randomized trial designed to evaluate a treatment for bipolar disorder. We present a comprehensive data analysis and a simulation study to evaluate the performance of the method. A software package entitled SAMON (R and SAS versions) that implements our methods is available at www.missingdatamatters.org.

Original languageEnglish (US)
JournalStatistical Methods in Medical Research
DOIs
Publication statusAccepted/In press - Jan 1 2018

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Keywords

  • Corrected estimator
  • exponential tilting
  • identifiability
  • missing not at random
  • plug-in estimator
  • smoothing

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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