Logical Inference on Efficacy in Subgroups and Their Combinations in Personalized Medicine

• Activity Number: 526
• Type: Topic Contributed
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Jason Hsu*
• Companies: The Ohio State University
• Keywords: Multiple Comparisons ; Subgroups ; Personalized Medicine

Abstract

Measuring treatment efficacy in a mixture population is a fundamental problem in personalized medicine development, in deciding which subgroup or combination of subgroups to treat. Such a development process typically involves comparing a new drug with a control through randomized clinical trials, and treatment efficacy is the relative effect between the new drug and the control. We show that some commonly used efficacy measures are not suitable for a mixture population. We also show that, while it is important to adjust for imbalance in the data using least squares means (LSmeans) (not marginal means) estimation, current practice over-extends the LSmeans concept when estimating the efficacy in a mixture population. Proposing a new principle called subgroup mixable estimation, we establish logical relationships among parameters that represent efficacy and develop a simultaneous inference procedure to confidently infer efficacy in subgroups and their combinations.