Sequential Advantage Selection for Optimal Treatment Regime

• Activity Number: 240
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Ailin Fan*+ and Wenbin Lu and Rui Song
• Companies: North Carolina State University and North Carolina State University and North Carolina State University
• Keywords: optimal treatment regime ; variable selection ; sequential selection ; prescriptive variables

Abstract

Variable selection for decision-making related to the optimal treatment regime in a clinical trial or an observational study is getting more attention. Current variable selection techniques focused on selecting variables that help make a good prediction. However, some variables that are poor in prediction but are critical for decision-making may be ignored, and hence new variable selection method is needed for decision-making. Gunter et al. (2011) proposed S-score which characterizes the magnitude of qualitative interaction of each variable with treatment individually. This is a quantity indicating the importance of each variable for decision-making. In this article, we developed a sequential advantage selection method based on the S-score. Our method selects qualitatively interacted variables sequentially, and hence excludes marginally important but jointly unimportant variables. What's more, with the proposed stopping criteria, our method can handle a large amount of covariates even if sample size is small. Simulation results show our method performs well in practical settings. We further applied our method to data from a clinical trial for depression.