Identifying Subpopulations with Differential Risk Benefit Profiles

• Activity Number: 393
• Type: Invited
• Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
• Sponsor: ENAR
• Author(s): Tianxi Cai*+
• Companies: Harvard University
• Keywords: personalized medicine ; treatment effect ; subgroup analysis ; biomarkers

Abstract

Accurate and individualized prediction of risk and treatment response plays a central role in successful disease prevention and treatment. Recent advancement in biological and genomic research has led to the discovery of a vast number of new markers predictive of disease outcomes. These new discoveries hold great potential for improving the prediction of clinical outcomes, and may lead to personalized, tailored medicine. To realize the goals of personalized medicine, significant efforts have been made on building risk prediction models and assessing subgroup-specific treatment effects. Many attempts have also been made to assess who may benefit most from a new treatment. However, most existing procedures focus on a single efficacy or adverse event outcome. In this talk, I'll discuss procedures that aim to identify subpopulations with differential risk benefit profiles with respect to the new treatment.