On Optimal Treatment Regimes Selection for Mean Survival Time

By Yuan Geng, Hao Helen Zhang, Wenbin Lu. 2014

30 citations recorded by the source corpus.

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. Copyright © 2014 John Wiley & Sons, Ltd.

Subfields: Statistics and Probability

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In Statistics and Probability

1. Ning Liu
2. Jian Tang
3. Brent R. Logan
4. Yanzhi Wang
5. Zhiyuan Xu
6. Ying Liu

Citations in the corpus

• Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data (2017, methodology): “For survival outcome in observational studies, reference [25] provided a linear regression method weighted by censoring probability and adjusted for propensity scores.”