https://orcid.org/0000-0002-0802-3413
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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.
Acknowledgments
We would like to thank Dr. Christina Wolfson for creating and sharing the dataset on survival with dementia derived from the CSHA. The CSHA was supported by the Seniors Independence Research Program, through the National Health Research and Development Program (NHRDP) of Health Canada (project 6606-3954-MC[S]). The progression of dementia project within the CSHA was supported by Pfizer Canada through the Health Activity Program of the Medical Research Council of Canada and the Pharmaceutical Manufacturers Association of Canada; by the NHRDP (project 6603-1417-302[R]); by Bayer; and by the British Columbia Health Research Foundation (projects 38 [93-2] and 34 [96-1]).
Disclosure statement
No potential conflict of interest was reported by the author(s).
Correction Statement
This article has been corrected with minor changes. These changes do not impact the academic content of the article.