Variable Selection with the Kernel Machine Cox Proportional Hazards Model for Optimal Treatment Strategy

• Activity Number: 69
• Type: Contributed
• Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Zifang Guo*+ and Wenbin Lu and Lexin Li
• Companies: Merck and North Carolina State University and North Carolina State University
• Keywords: optimal treatment strategy ; survival analysis ; variable selection ; semiparametric method

Abstract

In clinical studies, optimal treatment strategies are sets of rules for making subject specific treatment decisions that are determined based on each individual's various characteristics. In many such applications, censored survival time is of primary interest. To estimate the optimal treatment strategy, one usually needs to adjust the treatment and treatment-covariate interaction effect with the baseline covariates. However, the true function form of the baseline covariate effect is often complicated and nonlinear. In this presentation, we focus on optimal treatment strategy estimation in the survival framework, and propose the use of variable selection method with the kernel machine Cox proportional hazards model in estimating optimal treatment strategy. The proposed method allows nonparametric specification of the baseline covariate effects and leads to improved decision rules by incorporating shrinkage based variable selection method in estimation. Simulation studies are provided to illustrate the empirical performance of the proposed method.