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Smoothed quantile regression analysis of competing risks

Authors
Choi, SangbumKang, SangwookHuang, Xuelin
Issue Date
9월-2018
Publisher
WILEY
Keywords
censored quantile regression; cumulative incidence function; induced smoothing; variance estimation; weighted estimating equation
Citation
BIOMETRICAL JOURNAL, v.60, no.5, pp.934 - 946
Indexed
SCIE
SCOPUS
Journal Title
BIOMETRICAL JOURNAL
Volume
60
Number
5
Start Page
934
End Page
946
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/73173
DOI
10.1002/bimj.201700104
ISSN
0323-3847
Abstract
Censored quantile regression models, which offer great flexibility in assessing covariate effects on event times, have attracted considerable research interest. In this study, we consider flexible estimation and inference procedures for competing risks quantile regression, which not only provides meaningful interpretations by using cumulative incidence quantiles but also extends the conventional accelerated failure time model by relaxing some of the stringent model assumptions, such as global linearity and unconditional independence. Current method for censored quantile regressions often involves the minimization of the L-1-type convex function or solving the nonsmoothed estimating equations. This approach could lead to multiple roots in practical settings, particularly with multiple covariates. Moreover, variance estimation involves an unknown error distribution and most methods rely on computationally intensive resampling techniques such as bootstrapping. We consider the induced smoothing procedure for censored quantile regressions to the competing risks setting. The proposed procedure permits the fast and accurate computation of quantile regression parameter estimates and standard variances by using conventional numerical methods such as the Newton-Raphson algorithm. Numerical studies show that the proposed estimators perform well and the resulting inference is reliable in practical settings. The method is finally applied to data from a soft tissue sarcoma study.
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