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2019 Inference under Fine-Gray competing risks model with high-dimensional covariates
Jue Hou, Jelena Bradic, Ronghui Xu
Electron. J. Statist. 13(2): 4449-4507 (2019). DOI: 10.1214/19-EJS1562

Abstract

The purpose of this paper is to construct confidence intervals for the regression coefficients in the Fine-Gray model for competing risks data with random censoring, where the number of covariates can be larger than the sample size. Despite strong motivation from biomedical applications, a high-dimensional Fine-Gray model has attracted relatively little attention among the methodological or theoretical literature. We fill in this gap by developing confidence intervals based on a one-step bias-correction for a regularized estimation. We develop a theoretical framework for the partial likelihood, which does not have independent and identically distributed entries and therefore presents many technical challenges. We also study the approximation error from the weighting scheme under random censoring for competing risks and establish new concentration results for time-dependent processes. In addition to the theoretical results and algorithms, we present extensive numerical experiments and an application to a study of non-cancer mortality among prostate cancer patients using the linked Medicare-SEER data.

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Jue Hou. Jelena Bradic. Ronghui Xu. "Inference under Fine-Gray competing risks model with high-dimensional covariates." Electron. J. Statist. 13 (2) 4449 - 4507, 2019. https://doi.org/10.1214/19-EJS1562

Information

Received: 1 February 2018; Published: 2019
First available in Project Euclid: 6 November 2019

zbMATH: 07136622
MathSciNet: MR4028512
Digital Object Identifier: 10.1214/19-EJS1562

Subjects:
Primary: 62F30, 62N03
Secondary: 62J07

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Vol.13 • No. 2 • 2019
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