Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume 111, Issue 516, Pages 1509-1521Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/01621459.2016.1148611
Keywords
DC algorithm; Dose finding; Individualized dose rule; Risk bound; Weighted support vector regression
Categories
Funding
- NCI [P01 CA142538]
- [U01-N5082062]
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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 article, 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. Supplementary materials for this article are available online.
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