A Bayesian semiparametric method for analyzing length-biased data

Nusrat Harun, Bo Cai, Yu Shen

Research output: Contribution to journalArticlepeer-review

Abstract

Survival data obtained from prevalent cohort study designs are often subject to length-biased sampling. Frequentist methods including estimating equation approaches, as well as full likelihood methods, are available for assessing covariate effects on survival from such data. Bayesian methods allow a perspective of probability interpretation for the parameters of interest, and may easily provide the predictive distribution for future observations while incorporating weak prior knowledge on the baseline hazard function. There is lack of Bayesian methods for analyzing length-biased data. In this paper, we propose Bayesian methods for analyzing length-biased data under a proportional hazards model. The prior distribution for the cumulative hazard function is specified semiparametrically using I-Splines. Bayesian conditional and full likelihood approaches are developed for analyzing simulated and real data.

Original languageEnglish (US)
Pages (from-to)977-992
Number of pages16
JournalJournal of Applied Statistics
Volume48
Issue number6
DOIs
StatePublished - 2021

Keywords

  • Bayesian method
  • I-splines
  • length-biased data
  • prevalent cohort study
  • proportional hazards model

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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