A model-free variable screening method for optimal treatment regimes with high-dimensional survival data

被引:0
|
作者
Yang, Cheng-Han [1 ]
Cheng, Yu-Jen [2 ]
机构
[1] Univ Texas Hlth Sci Ctr Houston, Dept Biostat & Data Sci, 1200 Pressler St,W930, Houston, TX 77030 USA
[2] Natl Tsing Hua Univ, Inst Stat, 101,Sect 2,Kuang Fu Rd, Hsinchu 300044, Taiwan
关键词
High-dimensional survival data; Model-free variable screening; Optimal treatment regime; Outcome-dependent sampling; ESTIMATING INDIVIDUALIZED TREATMENT; PROPENSITY SCORE; SELECTION; CHEMOTHERAPY;
D O I
10.1093/biomet/asae022
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
We propose a model-free variable screening method for the optimal treatment regime with high-dimensional survival data. The proposed screening method provides a unified framework to select the active variables in a prespecified target population, including the treated group as a special case. Based on this framework, the optimal treatment regime is exactly the optimal classifier that minimizes a weighted misclassification error rate, with weights associated with survival outcome variables, the censoring distribution and a prespecified target population. Our main contribution involves reformulating the weighted classification problem into a classification problem within a hypothetical population, where the observed data can be viewed as a sample obtained from outcome-dependent sampling, with the selection probability inversely proportional to the weights. Consequently, we introduce the weighted Kolmogorov-Smirnov approach for selecting active variables in the optimal treatment regime, extending the conventional Kolmogorov-Smirnov method for binary classification. Additionally, the proposed screening method exhibits two levels of robustness. The first level of robustness is achieved because the proposed method does not require any model assumptions for the survival outcome on treatment and covariates, whereas the other is attained as the form of treatment regimes is allowed to be unspecified even without requiring convex surrogate loss, such as logit loss or hinge loss. As a result, the proposed screening method is robust to model misspecifications, and nonparametric learning methods such as random forests and boosting can be applied to those selected variables for further analysis. The theoretical properties of the proposed method are established. The performance of the proposed method is examined through simulation studies and illustrated by a lung cancer dataset.
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页数:18
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