Greediness Reduction Interaction Tree Algorithm for Subgroup Analysis in Clinical Trials

• Activity Number: 234
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Yi-Fan Chen*+ and Lisa Weissfeld
• Companies: University of Illinois at Chicago and University of Pittsburgh
• Keywords: evolutionary algorithm ; interaction tree ; random forests ; subgroup analysis

Abstract

With the advent of personalized medicine, the goal of an analysis is to identify subgroups that will receive the greatest benefit from a given treatment. The approach considered here centers on methods for clinical trials that are geared towards exploring heterogeneity between subjects and its impact on treatment response. One such approach is an extension of classification and regression trees (CART) based on the development of interaction trees. With these analyses it is possible to generate hypotheses for future clinical trials and to present the results in a readily accessible format. One major issue with this approach is the greediness of the algorithm and the difficulty of addressing it without losing the interpretability. We propose a greediness reduction interaction tree (GRIT) algorithm that integrates random forests and the evolutionary algorithm into the interaction trees algorithm and preserves the tree structure. Simulations show that this proposed method outperforms the interaction trees without accessing every predictor especially when the sample size is small. The strengths of the proposed method are demonstrated through a real data example.