Estimating individualized treatment rules with risk constraint - Huang, Xinyang; Xu, Jin (Biometrics, 2020)
Individualized treatment rules (ITRs) recommend treatments based on patient‐specific characteristics in order to maximize the expected clinical outcome. At the same time, the risks caused by various adverse events cannot be ignored. In this paper, we propose a method to estimate an optimal ITR that maximizes clinical benefit while having the overall risk controlled at a desired level. Our method works for a general setting of multi‐category treatment. The proposed procedure employs two shifted ramp losses to approximate the 0‐1 loss in the objective function and constraint, respectively, and transforms the estimation problem into a difference of convex functions (DC) programming problem. A relaxed DC algorithm is used to solve the nonconvex constrained optimization problem. Simulations and a real data example are used to demonstrate the finite sample performance of the proposed method.