Incorporating prior beliefs about selection bias into the analysis of randomized trials with missing outcomes.

Daniel O. Scharfstein, Michael J. Daniels, James M. Robins

Research output: Contribution to journalArticlepeer-review

Abstract

In randomized studies with missing outcomes, non-identifiable assumptions are required to hold for valid data analysis. As a result, statisticians have been advocating the use of sensitivity analysis to evaluate the effect of varying assumptions on study conclusions. While this approach may be useful in assessing the sensitivity of treatment comparisons to missing data assumptions, it may be dissatisfying to some researchers/decision makers because a single summary is not provided. In this paper, we present a fully Bayesian methodology that allows the investigator to draw a 'single' conclusion by formally incorporating prior beliefs about non-identifiable, yet interpretable, selection bias parameters. Our Bayesian model provides robustness to prior specification of the distributional form of the continuous outcomes.

Original languageEnglish (US)
Pages (from-to)495-512
Number of pages18
JournalBiostatistics (Oxford, England)
Volume4
Issue number4
DOIs
StatePublished - Oct 2003

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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