Compound Poisson frailty model with a gamma process prior for the baseline hazard: accounting for a cured fraction

Maryam Rahmati, Parisa Rezanejad Asl, Javad Mikaeli, Hojjat Zeraati and Aliakbar Rasekhi

Journal of Applied Statistics, 2022, vol. 49, issue 13, 3377-3391

Abstract: Cox model and traditional frailty models assume that all individuals will eventually experience the event of interest. This assumption is often overlooked, and situations will arise where it is not realistic. We introduce Compound Poisson frailty model for survival analysis to deal with populations in which some of the individuals will not experience the event of interest. This model assumes that the target population is a mixture of individuals with zero frailty and those with positive frailty. In this paper, we consider a compound Poisson frailty model for right-censored event times from a Bayesian perspective and compute the Bayesian estimator using the Markov Chain Monte Carlo method, where a Gamma process prior is adopted for the baseline hazard function. Furthermore, we evaluate the approach using simulation studies and demonstrate the methodology by analyzing the data from achalasia patient cohort.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2021.1947997 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:49:y:2022:i:13:p:3377-3391

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2021.1947997

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().