Online Program

Precision Medicine: Subgroup Identification in Longitudinal Pharmacogenetic Studies (306784)

Hongmei Jiang, Northwestern University 
Bankole Johnson, University of Maryland 
*Lei Liu, Washington University in St. Louis 
Xiaogang Su, UTEP 
Yishu Wei, Northwestern University 
Lihui Zhao, Northwestern University 

Keywords: Interaction tree, longitudinal trajectories, splines, precision medicine

In clinical studies, treatment effect may be heterogeneous among patients. It is of interest to identify subpopulations which benefit most from the treatment, regardless of the treatment's overall performance. In this study we are interested in subgroup identification methods in longitudinal studies when nonlinear trajectory patterns are present. Under such a situation, evaluation of the treatment effect entails comparing longitudinal trajectories. We propose a tree-structured subgroup identification method, termed interaction tree for longitudinal trajectories or IT-LT in short, which combines mixed effects models with regression splines to model the nonlinear progression patterns among repeated measures. The general framework of IT-LT has the great flexibility to incorporate missing data, irregular observation times, and other complexities that are commonly encountered in longitudinal studies. Extensive simulation studies are conducted to evaluate its performance and an application to an alcohol addiction pharmacogenetic trial demonstrates its advantage.