A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling

Chiung Yu Huang, Jing Qin, Dean A. Follmann

Research output: Contribution to journalArticle

Abstract

This paper considers semiparametric estimation of the Cox proportional hazards model for right-censored and length-biased data arising from prevalent sampling. To exploit the special structure of length-biased sampling, we propose a maximum pseudo-profile likelihood estimator, which can handle time-dependent covariates and is consistent under covariate-dependent censoring. Simulation studies show that the proposed estimator is more efficient than its competitors. A data analysis illustrates the methods and theory.

Original languageEnglish (US)
Pages (from-to)199-210
Number of pages12
JournalBiometrika
Volume99
Issue number1
DOIs
StatePublished - Mar 1 2012

Keywords

  • Approximate likelihood
  • Cross-sectional sampling
  • Product-limit estimator
  • Random truncation
  • Screening trials

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Fingerprint Dive into the research topics of 'A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling'. Together they form a unique fingerprint.

  • Cite this