On Bayesian methods of exploring qualitative interactions for targeted treatment

Wei Chen, Debashis Ghosh, Trivellore E. Raghunathan, Maxim Norkin, Daniel J. Sargent, Gerold Bepler

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Providing personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in AppendixA.

Original languageEnglish (US)
Pages (from-to)3693-3707
Number of pages15
JournalStatistics in Medicine
Issue number28
StatePublished - Dec 10 2012


  • Clinical trial
  • Interaction
  • Predictive marker
  • Prognostic marker
  • Subgroup

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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