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

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  • Maryam Rahmati
  • Parisa Rezanejad Asl
  • Javad Mikaeli
  • Hojjat Zeraati
  • Aliakbar Rasekhi

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.

Suggested Citation

  • Maryam Rahmati & Parisa Rezanejad Asl & Javad Mikaeli & Hojjat Zeraati & Aliakbar Rasekhi, 2022. "Compound Poisson frailty model with a gamma process prior for the baseline hazard: accounting for a cured fraction," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(13), pages 3377-3391, October.
    • Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3377-3391
    DOI: 10.1080/02664763.2021.1947997
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