Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2015 Mar 30;34(7):1169-84.
doi: 10.1002/sim.6397. Epub 2014 Dec 16.

On Optimal Treatment Regimes Selection for Mean Survival Time

Affiliations
Free PMC article

On Optimal Treatment Regimes Selection for Mean Survival Time

Yuan Geng et al. Stat Med. .
Free PMC 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.

Keywords: adaptive LASSO; censored regression; mean survival time; optimal treatment regime; variable selection.

Figures

Figure 1
Figure 1
The Kaplan-Meier survival curves for four treatment groups and patients following the estimated optimal treatment regime with the linear model for baseline.

Similar articles

See all similar articles

Cited by 7 articles

See all "Cited by" articles

Publication types

MeSH terms

Substances

LinkOut - more resources

Feedback