Title | Q-LEARNING WITH CENSORED DATA. |
Publication Type | Journal Article |
Year of Publication | 2012 |
Authors | Goldberg, Yair, and Michael R. Kosorok |
Journal | Ann Stat |
Volume | 40 |
Issue | 1 |
Pagination | 529-560 |
Date Published | 2012 Feb 01 |
ISSN | 0090-5364 |
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. |
DOI | 10.1214/12-AOS968 |
Alternate Journal | Ann Stat |
Original Publication | Q-learning with censored data. |
PubMed ID | 22754029 |
PubMed Central ID | PMC3385950 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States P01 CA142538-01 / CA / NCI NIH HHS / United States P01 CA142538-02 / CA / NCI NIH HHS / United States P01 CA142538-03 / CA / NCI NIH HHS / United States |
Q-LEARNING WITH CENSORED DATA.
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