title: Personalized Dose Finding Using Outcome Weighted Learning.
abstract: In dose-finding clinical trials, it is becoming increasingly important to account for individual level heterogeneity while searching for optimal doses to ensure an optimal individualized dose rule (IDR) maximizes the expected beneficial clinical outcome for each individual. In this paper, we advocate a randomized trial design where candidate dose levels assigned to study subjects are randomly chosen from a continuous distribution within a safe range. To estimate the optimal IDR using such data, we propose an outcome weighted learning method based on a nonconvex loss function, which can be solved efficiently using a difference of convex functions algorithm. The consistency and convergence rate for the estimated IDR are derived, and its small-sample performance is evaluated via simulation studies. We demonstrate that the proposed method outperforms competing approaches. Finally, we illustrate this method using data from a cohort study for Warfarin (an anti-thrombotic drug) dosing.
authors.affiliation: Assistant Professor, Department of Biostatistics, Vanderbilt University, Nashville, TN 37203.
authors.forename: Guanhua
authors.initials: G
authors.lastname: Chen
authors.affiliation: Professor, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599.
authors.forename: Donglin
authors.initials: D
authors.lastname: Zeng
authors.affiliation: W. R. Kenan, Jr. Distinguished Professor and Chair, Department of Biostatistics, and Professor, Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC 27599.
authors.forename: Michael R
authors.initials: MR
authors.lastname: Kosorok
keywords: DC Algorithm; Dose Finding; Individualized Dose Rule; Risk Bound; Weighted Support Vector Regression