Finding Optimal Treatment Dose Using Outcome-Weighted Learning

• Activity Number: 293
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Guanhua Chen*+ and Michael Kosorok and Donglin Zeng
• Companies: University of North Carolina at Chapel Hill and University of North Carolina at Chapel Hill and University of North Carolina at Chapel Hill
• Keywords: Dose Finding ; Individualized Treatment Rule ; Weighted Support Vector Regression ; Difference Convex Algorithm

Abstract

Recently, there are increasing needs for considering individual heterogeneity in finding the optimal treatment dose. In particular, instead of determining a fixed dose for all patients, it is desirable to find a decision rule as a function of patient characteristics such that the expected clinical outcome in the population level is maximized. In this paper, we propose a randomized trial design for optimal dose finding and provide a corresponding analysis method. We show that our proposed dose finding method using randomized trial data can be regarded as an inverse probability weighting estimator of expected clinical outcome. Further, we show the estimation problem is equivalent to solve a weighted regression problem with a truncated ?_1 loss function. An efficient difference convex algorithm is proposed to solve the associated non-convex optimization problem. We derive the asymptotic consistency of the estimated decision rule. In addition, the performance of the proposed and competitive methods are illustrated through both simulation examples and a real data example for dosage identification for Warfarin (an anti-thrombosis drug).