On Bayesian methods of exploring qualitative interactions for targeted treatment.
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.
Department of Oncology, School of Medicine, Wayne State University, Detroit, MI 48201, USA. chenw@karmanos.org
Wei
W
Chen
Debashis
D
Ghosh
Trivellore E
TE
Raghunathan
Maxim
M
Norkin
Daniel J
DJ
Sargent
Gerold
G
Bepler
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D000911:Antibodies, Monoclonal; D061067:Antibodies, Monoclonal, Humanized; D000970:Antineoplastic Agents; D005819:Genetic Markers; D011799:Quinazolines; D016190:Carboplatin; D066246:ErbB Receptors; D017239:Paclitaxel; D000068818:Cetuximab; D000077156:Gefitinib