Sparse Concordance-Assisted Learning for Optimal Treatment Decision

• Activity Number: 504 - Model/Variable Selection
• Type: Contributed
• Date/Time: Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Shuhan Liang* and Wenbin Lu and Rui Song and Lan Wang
• Companies: North Carolina State University and North Carolina State University and NC State University and University of Minnesota
• Keywords: optimal treatment regime ; variable selection ; L1 norm support vector machine ; concordance-assisted learning

Abstract

To find optimal decision rule, Fan et al. proposed an innovative concordance-assisted learning algorithm which is based on maximum rank correlation estimator. It makes better use of the available information through pairwise comparison. However the objective function is discontinuous and computationally hard to optimize. In this paper, we consider a convex surrogate loss function to solve this problem. In addition, our algorithm ensures sparsity of decision rule and makes it easy to interpret. We then give the L2 error bound of the estimated coefficients under ultra-high dimension. Simulation results of various settings and application to STAR*D both illustrate that the proposed method can still estimate optimal treatment regime successfully when the number of covariates is large.