INFERENCE FOR LOW-DIMENSIONAL COVARIATES IN A HIGH-DIMENSIONAL ACCELERATED FAILURE TIME MODEL

Hao Chai; Qingzhao Zhang; Jian Huang; Shuangge Ma

doi:10.5705/ss.202016.0449

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INFERENCE FOR LOW-DIMENSIONAL COVARIATES IN A HIGH-DIMENSIONAL ACCELERATED FAILURE TIME MODEL

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INFERENCE FOR LOW-DIMENSIONAL COVARIATES IN A HIGH-DIMENSIONAL ACCELERATED FAILURE TIME MODEL

Hao Chai, Qingzhao Zhang, Jian Huang and Shuangge Ma

Statistica Sinica, Vol.29(2), pp.877-894

04/01/2019

DOI: 10.5705/ss.202016.0449

PMCID: PMC6502249

PMID: 31073263

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https://www.ncbi.nlm.nih.gov/pmc/articles/6502249View

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Abstract

Data with high-dimensional covariates are now commonly encountered. Compared to other types of responses, research on high-dimensional data with censored survival responses is still relatively limited, and most of the existing studies have been focused on estimation and variable selection. In this study, we consider data with a censored survival response, a set of low-dimensional covariates of main interest, and a set of high-dimensional covariates that may also affect survival. The accelerated failure time model is adopted to describe survival. The goal is to conduct inference for the effects of low-dimensional covariates, while properly accounting for the high-dimensional covariates. A penalization-based procedure is developed, and its validity is established under mild and widely adopted conditions. Simulation suggests satisfactory performance of the proposed procedure, and the analysis of two cancer genetic datasets demonstrates its practical applicability.

Mathematics

Physical Sciences

Science & Technology

Statistics & Probability

Details

Title: Subtitle: INFERENCE FOR LOW-DIMENSIONAL COVARIATES IN A HIGH-DIMENSIONAL ACCELERATED FAILURE TIME MODEL
Creators: Hao Chai - Yale University
Qingzhao Zhang - Xiamen University
Jian Huang - Hong Kong Polytechnic University
Shuangge Ma - Yale University
Resource Type: Journal article
Publication Details: Statistica Sinica, Vol.29(2), pp.877-894
DOI: 10.5705/ss.202016.0449
PMID: 31073263
PMCID: PMC6502249
NLM abbreviation: Stat Sin
ISSN: 1017-0405
eISSN: 1996-8507
Publisher: STATISTICA SINICA
Number of pages: 18
Grant note: R21CA191383; R01CA204120 / National Institutes of Health 11401561; 210100113 / National Natural Science Foundation of China
Language: English
Date published: 04/01/2019
Academic Unit: Statistics and Actuarial Science
Record Identifier: 9984257631502771

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