Decision-Making in Post Clinical Trials

• Activity Number: 470
• Type: Invited
• Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Heping Zhang*
• Companies: Yale University
• Keywords: decision trees ; recursive partitioning ; personalized medicine ; clinical trials

Abstract

Double-blind, randomized clinical trials are the preferred approach to demonstrating the effectiveness of one treatment against another. The comparison is, however, made on the average group effects. While patients and clinicians have always struggled to understand why patients respond differently to the same treatment, and while much hope has been held for the nascent field of predictive biomarkers (e.g. genetic markers), there is still much utility in exploring whether it is possible to estimate treatment efficacy based on demographic and baseline variables including biomarkers. To address this issue, we focused on a concept of the relative effectiveness of treatments that is of particular importance in personalized medicine. The method can identify groups of patients that are more likely to respond one treatment than the other, in contrast to the tradition approach that searches for a superior treatment in a larger population. We developed an automated algorithm to construct decision trees and performed extensive simulation to evaluate our algorithm. We analyzed data from clinical trials to illustrate the practical potential of our method.