On optimal treatment regimes selection for mean survival time.

TitleOn optimal treatment regimes selection for mean survival time.
Publication TypeJournal Article
Year of Publication2015
AuthorsGeng, Yuan, Hao Helen Zhang, and Wenbin Lu
JournalStat Med
Volume34
Issue7
Pagination1169-84
Date Published2015 Mar 30
ISSN1097-0258
KeywordsAcquired Immunodeficiency Syndrome, Anti-HIV Agents, Biostatistics, Clinical Trials as Topic, Computer Simulation, Humans, Kaplan-Meier Estimate, Least-Squares Analysis, Models, Statistical, Precision Medicine, Regression Analysis, Statistics, Nonparametric, Survival Rate
Abstract

In clinical studies with time-to-event as a primary endpoint, one main interest is to find the best treatment strategy to maximize patients' mean survival time. Due to patient's heterogeneity in response to treatments, great efforts have been devoted to developing optimal treatment regimes by integrating individuals' clinical and genetic information. A main challenge arises in the selection of important variables that can help to build reliable and interpretable optimal treatment regimes as the dimension of predictors may be high. In this paper, we propose a robust loss-based estimation framework that can be easily coupled with shrinkage penalties for both estimation of optimal treatment regimes and variable selection. The asymptotic properties of the proposed estimators are studied. Moreover, a model-free estimator of restricted mean survival time under the derived optimal treatment regime is developed, and its asymptotic property is studied. Simulations are conducted to assess the empirical performance of the proposed method for parameter estimation, variable selection, and optimal treatment decision. An application to an AIDS clinical trial data set is given to illustrate the method.

DOI10.1002/sim.6397
Alternate JournalStat Med
Original PublicationOn optimal treatment regimes selection for mean survival time.
PubMed ID25515005
PubMed Central IDPMC4355217
Grant ListR01 CA140632 / CA / NCI NIH HHS / United States
R01CA140632 / CA / NCI NIH HHS / United States
P01CA142538 / CA / NCI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
P30 AI050410 / AI / NIAID NIH HHS / United States
Project: