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A fast algorithm for the accelerated failure time model with high-dimensional time-to-event data

Authors
Choi, TaehwaChoi, Sangbum
Issue Date
2-11월-2021
Publisher
TAYLOR & FRANCIS LTD
Keywords
Coordinate descent; kernel smoothing; linear model; logistic loss; regularization; survival analysis; variable selection
Citation
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v.91, no.16, pp.3385 - 3403
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
Volume
91
Number
16
Start Page
3385
End Page
3403
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/135780
DOI
10.1080/00949655.2021.1927034
ISSN
0094-9655
Abstract
We propose the logistic-kernel smoothing procedure for the semiparametric accelerated failure time (AFT) model with high-dimensional right-censored data. The resulting estimating procedure permits fast and accurate computation of regression parameter estimates and standard errors while preserving the same asymptotic properties as those from the non-smoothed rank estimating function. In addition, we provide an efficient numerical algorithm for obtaining a complete regularization path to facilitate adaptive variable selection in the AFT model. This can be done by using a second-order approximation of the smoothed estimating function and coordinate decent algorithm. Through extensive simulation studies, we examine several well-known penalties and show that our method is robust and computationally efficient with minimal loss of precision. Application to primary biliary cirrhosis (PBC) data demonstrates the utility of the proposed method in routine survival data analysis.
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