Commensurate priors for incorporating historical information in clinical trials using general and generalized linear models

Brian P. Hobbs, Daniel J. Sargent, Bradley P. Carlin

Research output: Contribution to journalArticle

63 Scopus citations

Abstract

Assessing between-study variability in the context of conventional random-effects meta-analysis is notoriously diffcult when incorporating data from only a small number of historical studies. In order to borrow strength, historical and current data are often assumed to be fully homogeneous, but this can have drastic consequences for power and Type I error if the historical information is biased. In this paper, we propose empirical and fully Bayesian modifications of the commensurate prior model (Hobbs et al. 2011) extending Pocock (1976), and evaluate their frequentist and Bayesian properties for incorporating patient-level historical data using general and generalized linear mixed regression models. Our proposed commensurate prior models lead to preposterior admissible estimators that facilitate alternative bias-variance trade-offs than those offered by pre-existing methodologies for incorporating historical data from a small number of historical studies. We also provide a sample analysis of a colon cancer trial comparing time-to-disease progression using a Weibull regression model.

Original languageEnglish (US)
Pages (from-to)639-674
Number of pages36
JournalBayesian Analysis
Volume7
Issue number3
DOIs
StatePublished - 2012

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Keywords

  • Bayesian analysis
  • Clinical trials
  • Correlated data
  • Historical controls
  • Meta-analysis
  • Survival analysis

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability

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