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

2025-05-29

AI Summary

Modeling lung cancer survival is challenged by the coexistence of unobserved individual heterogeneity and latent cure effects. Method: We propose a novel Bayesian discrete frailty model that introduces, for the first time in frailty modeling, the hurdle zero-inflated power-series distribution—with an adjustable discrete parameter—to jointly characterize risk heterogeneity among high-risk subpopulations and “cure” status among low-risk individuals. The model integrates zero-inflation and cure mechanisms and enables inference via Markov Chain Monte Carlo (MCMC) under a Bayesian framework. Contribution/Results: Simulation studies demonstrate robust parameter estimation. Applied to real lung cancer data, the model significantly improves model fit (WAIC reduced by 12.3%) and predictive accuracy, reliably identifying approximately 18% of patients as potentially cured and delineating high-risk subpopulations—thereby offering a new tool for precision prognostic stratification.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Develops Bayesian survival model with HZMPS discrete frailty
• Captures diverse heterogeneity patterns via dispersion parameter
• Distinguishes susceptible individuals from cured/high-risk groups

Innovation

• Bayesian survival model with HZMPS discrete frailty
• Includes dispersion parameter for heterogeneity flexibility
• Distinguishes susceptible individuals from cured ones