Q-LEARNING WITH CENSORED DATA.

TitleQ-LEARNING WITH CENSORED DATA.
Publication TypeJournal Article
Year of Publication2012
AuthorsGoldberg, Yair, and Michael R. Kosorok
JournalAnn Stat
Volume40
Issue1
Pagination529-560
Date Published2012 Feb 01
ISSN0090-5364
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.

DOI10.1214/12-AOS968
Alternate JournalAnn Stat
Original PublicationQ-learning with censored data.
PubMed ID22754029
PubMed Central IDPMC3385950
Grant ListP01 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
Project: