Fine-Gray proportional subdistribution hazards model for competing risks data under length-biased sampling

Feipeng Zhang, Heng PENG*, Yong Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we study the Fine-Gray proportional subdistribution hazards model for the competing risks data under length-biased sampling. To exploit the special structure of length-biased sampling, we propose an unbiased estimating equation estimator, which can handle both covariateindependent censoring and the covariate-dependent censoring. The large sample properties of the proposed estimator are derived, model-checking techniques for the model adequacy are developed, and the pointwise confidence intervals and the simultaneous confidence bands for the predicted cumulative incidence functions are also constructed. Simulation studies are conducted to assess the finite sample performance of the proposed estimator. An application to the employment data illustrates the method and theory.

Original languageEnglish
Pages (from-to)107-122
Number of pages16
JournalStatistics and its Interface
Volume12
Issue number1
DOIs
Publication statusPublished - 2019

Scopus Subject Areas

  • Statistics and Probability
  • Applied Mathematics

User-Defined Keywords

  • Competing risks data
  • Fine-Gray model
  • Lengthbiased sampling
  • Model checking techniques

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