Article first published online: 26 JUN 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 31, Issue 28, pages 3693–3707, 10 December 2012
How to Cite
Chen, W., Ghosh, D., Raghunathan, T. E., Norkin, M., Sargent, D. J. and Bepler, G. (2012), On Bayesian methods of exploring qualitative interactions for targeted treatment. Statist. Med., 31: 3693–3707. doi: 10.1002/sim.5429
- Issue published online: 23 NOV 2012
- Article first published online: 26 JUN 2012
- Manuscript Accepted: 3 APR 2012
- Manuscript Revised: 27 FEB 2012
- Manuscript Received: 3 SEP 2010
- National Institutes of Health through Karmanos Cancer Institute. Grant Numbers: P30 CA022453, RO1 CA129102
- predictive marker;
- prognostic marker;
- clinical trial
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 Appendix A. Copyright © 2012 John Wiley & Sons, Ltd.