Cured Fraction Models on Survival Data and Covariates with a Bayesian Parametric Estimation Methods

Umar Yusuf Madaki; Babangida Ibrahim Babura; Muhammad Sani; Ibrahim Abdullahi

Cured Fraction Models on Survival Data and Covariates with a Bayesian Parametric Estimation Methods

Authors

Umar Yusuf Madaki , Babangida Ibrahim Babura , Muhammad Sani , Ibrahim Abdullahi

Published:

2023-03-31

Issue:

Vol. 1 No. 1 (2023): March

Keywords:

Bayesian analysis, Beta-Weibull distribution, Cure fraction models, Survival analysis, MCMC algorithm

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Madaki, U. Y., Babura, B. I., Sani, M., & Abdullahi, I. (2023). Cured Fraction Models on Survival Data and Covariates with a Bayesian Parametric Estimation Methods. Sigma&Mu: Journal of Mathematics, Statistics and Data Science, 1(1), 1–8. Retrieved from https://journals.balaipublikasi.id/index.php/sigmamu/article/view/45

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Abstract

A cure fraction models are usually meant for survival data that contains a proportion of non subject individuals for the event under study. In order to estimate the cure fraction, two models namely mixture model and non-mixture model were commonly deployed. In this work, mixture and non-mixture cure fraction models were presented with survival data structure based on the beta-Weibull distribution. The beta-Weibull distribution is a four parameter distribution developed in this work as an alternative extension to the Weibull distribution in the analysis of lifetime data. The proposed extension allows the inclusion of covariates analysis in the model, in which the estimation of parameters were done under Bayesian approach using Gibbs sampling methods

References

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Cured Fraction Models on Survival Data and Covariates with a Bayesian Parametric Estimation Methods

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