Identifying optimal biomarker combinations for treatment selection via a robust kernel method.

ABSTRACT: Treatment-selection markers predict an individual's response to different therapies, thus allowing for the selection of a therapy with the best predicted outcome. A good marker-based treatment-selection rule can significantly impact public health through the reduction of the disease burden in a cost-effective manner. Our goal in this article is to use data from randomized trials to identify optimal linear and nonlinear biomarker combinations for treatment selection that minimize the total burden to the population caused by either the targeted disease or its treatment. We frame this objective into a general problem of minimizing a weighted sum of 0-1 loss and propose a novel penalized minimization method that is based on the difference of convex functions algorithm (DCA). The corresponding

SUBMITTER: Huang Y 

PROVIDER: S-EPMC4277554 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature