On optimal treatment regimes selection for mean survival time
Year: 2014
Type: article
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.
Source: Statistics in Medicine
DOI: 10.1002/sim.6397
Authors: Yuan Geng, Hao Helen Zhang, Wenbin Lu
Institutions: North Carolina State University, University of Arizona
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, National Institute for Health and Care Research, National Institutes of Health, National Cancer Institute, National Institute of Allergy and Infectious Diseases, Division of Biological Infrastructure, Center for AIDS Research, University of North Carolina at Chapel Hill
OpenAlex ID: W2142295428