Selecting Treatment Strategy Based on Individual Characteristics Using Data from Randomized Clinical Trials

• Activity Number: 232
• Type: Contributed
• Date/Time: Monday, August 1, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Rui Wang*+ and David Alan Schoenfeld
• Companies: Harvard School of Public Health and Massachusetts General Hospital
• Keywords: clinical trials ; treatment effect heterogeneity ; qualitative interaction

Abstract

The primary objective of a Randomized Clinical Trial is to investigate whether one treatment is better than its alternatives on average. However, treatment effects may vary across different patient subpopulations. In contrast to demonstrating one treatment is superior to another on the average sense, one is often more concerned with the question that, for a particular patient, or a group of patients with similar characteristics, which treatment strategy is most appropriate to achieve a desired outcome. We propose a new permutation test for the null hypothesis of no interaction effects for any covariate. The proposed test allows us to consider the interaction effects of many covariates simultaneously without having to group subjects into subsets based on pre-specified criteria and applies generally to randomized clinical trials of multiple treatments. In addition, we introduce a new analytical framework for treatment selection strategy based on individual characteristics. The proposed framework provides one way to generalize the concept of qualitative interaction to multiple covariates in the absence of pre-specified patient subsets.