Variable Selection for High-dimensional Cox Model with Error Rate Control

HE Baihua, SHI Hongwei, GUO Xu, ZOU Changliang, ZHU Lixing

Journal of Systems Science & Complexity ›› 2025, Vol. 38 ›› Issue (3) : 1162-1185.

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Journal of Systems Science & Complexity ›› 2025, Vol. 38 ›› Issue (3) : 1162-1185. DOI: 10.1007/s11424-024-3484-6

Variable Selection for High-dimensional Cox Model with Error Rate Control

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Abstract

Simultaneously finding active predictors and controlling the false discovery rate (FDR) for high-dimensional survival data is an important but challenging statistical problem. In this paper, the authors propose a novel variable selection procedure with error rate control for the high-dimensional Cox model. By adopting a data-splitting strategy, the authors construct a series of symmetric statistics and then utilize the symmetry property to derive a data-driven threshold to achieve error rate control. The authors establish finite-sample and asymptotic FDR control results under some mild conditions. Simulation results as well as a real data application show that the proposed approach successfully controls FDR and is often more powerful than the competing approaches.

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Data-splitting / false discovery rate / high-dimensional survival data / symmetry

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HE Baihua , SHI Hongwei , GUO Xu , ZOU Changliang , ZHU Lixing. Variable Selection for High-dimensional Cox Model with Error Rate Control. Journal of Systems Science & Complexity, 2025, 38(3): 1162-1185 https://doi.org/10.1007/s11424-024-3484-6

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Funding

This research was supported by the National Natural Science Foundation of China under Grant Nos. 12301364, 12322112, 12071038, 11925106, 12231011, 11931001, 12226007, 12326325, and 12131006, the National Key R&D Program of China under Grant Nos. 2022YFA1003703 and 2022YFA1003800, the Natural Science Foundation of Anhui Province under Grant No. 2308085QA09, the Fundamental Research Funds for the Central Universities under Grant No. 2243200006, the Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences under Grant No. CI2023C063YLL, and the University Grant Council of Hong Kong.
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