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

Daniel O. Scharfstein, Aidan McDermott

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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

Original languageEnglish (US)
Pages (from-to)1439-1456
Number of pages18
JournalStatistical Methods in Medical Research
Issue number5
StatePublished - May 1 2019


  • 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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