On Bayesian methods of exploring qualitative interactions for targeted treatment.

Abstract:

: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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Chen W,Ghosh D,Raghunathan TE,Norkin M,Sargent DJ,Bepler G

doi

10.1002/sim.5429

subject

Has Abstract

pub_date

2012-12-10 00:00:00

pages

3693-707

issue

28

eissn

0277-6715

issn

1097-0258

journal_volume

31

pub_type

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