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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.

SUBMITTER: Goldberg Y

PROVIDER: S-EPMC3385950 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Q-LEARNING WITH CENSORED DATA.

Goldberg Yair Y Kosorok Michael R MR

Annals of statistics 20120201 1

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 ...[more]

PMID: 22754029

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