A Bayesian survival model induced by hurdle zero-modified power series discrete frailty with dispersion: an application in lung cancer

TL;DR

This paper introduces a flexible Bayesian survival model with hurdle zero-modified power series frailty, capturing heterogeneity and susceptibility differences in clinical data, demonstrated through simulation and lung cancer application.

Contribution

It proposes a novel Bayesian frailty model with dispersion for better heterogeneity modeling in survival analysis, applied to lung cancer data.

Findings

Enhanced flexibility in modeling heterogeneity.

Effective differentiation of susceptible and cured individuals.

Robust inference with limited data.

Abstract

Frailty survival models are widely used to capture unobserved heterogeneity among individuals in clinical and epidemiological research. This paper introduces a Bayesian survival model that features discrete frailty induced by the hurdle zero-modified power series (HZMPS) distribution. A key characteristic of HZMPS is the inclusion of a dispersion parameter, enhancing flexibility in capturing diverse heterogeneity patterns. Furthermore, this frailty specification allows the model to distinguish individuals with higher susceptibility to the event of interest from those potentially cured or no longer at risk. We employ a Bayesian framework for parameter estimation, enabling the incorporation of prior information and robust inference, even with limited data. A simulation study is performed to explore the limits of the model. Our proposal is also applied to a lung cancer study, in which patient variability plays a crucial role in disease progression and treatment response. The findings of this study highlight the importance of more flexible frailty models in survival data analysis and emphasize the potential of the Bayesian approach to modeling heterogeneity in biomedical studies.