Bayesian extension of the Weibull AFT shared frailty model with generalized family of distributions for enhanced survival analysis using censored data

• Published in: Journal of applied statistics Vol. 51; no. 15; pp. 3125 - 3153
• Main Authors: Parvej, Mohammad, Ali Khan, Athar
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 17-11-2024; Taylor & Francis Ltd
• Subjects: AFT; Bayesian analysis; censored data; Censored data (mathematics); Data analysis; Failure analysis; Failure times; Heterogeneity; LOOIC; Parameter estimation; shared-frailty; STAN; Statistical inference; Survival; Type I half-logistic-G; type II exponentiated half logistic-G; type II half logistic-G; WAIC; Weibull distribution
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664763.2024.2338404

In survival analysis, the Accelerated Failure Time (AFT) shared frailty model is a widely used framework for analyzing time-to-event data while accounting for unobserved heterogeneity among individuals. This paper extends the traditional Weibull AFT shared frailty model using half logistic-G family of distributions (Type I, Type II and Type II exponentiated) through Bayesian methods. This approach offers flexibility in capturing covariate influence and handling heavy-tailed frailty distributions. Bayesian inference with MCMC provides parameter estimates and credible intervals. Simulation studies show improved model predictive performance compared to existing models, and real-world applications demonstrate its practical utility. In summary, our Bayesian Weibull AFT shared frailty model with Type I, Type II and Type II exponentiated half logistic-G family distributions enhances time-to-event data analysis, making it a versatile tool for survival analysis in various fields using STAN in R.