Smoothed quantile regression analysis of competing risks

Sangbum Choi, Sangwook Kang, Xuelin Huang

Research output: Contribution to journalArticle

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 L1-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.

Original languageEnglish
Pages (from-to)934-946
Number of pages13
JournalBiometrical Journal
Volume60
Issue number5
DOIs
Publication statusPublished - 2018 Sep 1

Fingerprint

Competing Risks
Quantile Regression
Regression Analysis
Censored Regression
Covariates
Accelerated Failure Time Model
Multiple Roots
Newton-Raphson
Variance Estimation
Soft Tissue
Estimating Equation
Bootstrapping
Resampling
Quantile
Linearity
Convex function
Smoothing
Numerical Study
Incidence
Regression Model

Keywords

  • censored quantile regression
  • cumulative incidence function
  • induced smoothing
  • variance estimation
  • weighted estimating equation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Smoothed quantile regression analysis of competing risks. / Choi, Sangbum; Kang, Sangwook; Huang, Xuelin.

In: Biometrical Journal, Vol. 60, No. 5, 01.09.2018, p. 934-946.

Research output: Contribution to journalArticle

Choi, Sangbum ; Kang, Sangwook ; Huang, Xuelin. / Smoothed quantile regression analysis of competing risks. In: Biometrical Journal. 2018 ; Vol. 60, No. 5. pp. 934-946.
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