title: Q-LEARNING WITH CENSORED DATA.
abstract: We develop methodology for a multistage-decision problem with flexible number of stages in which the rewards are survival times that are subject to censoring. We present a novel Q-learning algorithm that is adjusted for censored data and allows a flexible number of stages. We provide finite sample bounds on the generalization error of the policy learned by the algorithm, and show that when the optimal Q-function belongs to the approximation space, the expected survival time for policies obtained by the algorithm converges to that of the optimal policy. We simulate a multistage clinical trial with flexible number of stages and apply the proposed censored-Q-learning algorithm to find individualized treatment regimens. The methodology presented in this paper has implications in the design of personalized medicine trials in cancer and in other life-threatening diseases.
authors: Department of Biostatistics, The University of North Carolina At Chapel Hill, Chapel Hill, NC 27599, U.S.A.
authors: Yair
authors: Y
authors: Goldberg
authors: Michael R
authors: MR
authors: Kosorok