Generalised Lindley shared additive frailty regression model for bivariate survival data

• Authors: Arvind Pandey , David D. Hanagal , Shikhar Tyagi

Abstracts

EN

Frailty models are the possible choice to counter the problem of the unobserved heterogeneity in individual risks of disease and death. Based on earlier studies, shared frailty models can be utilised in the analysis of bivariate data related to survival times (e.g. matched pairs experiments, twin or family data). In this article, we assume that frailty acts additively to the hazard rate. A new class of shared frailty models based on generalised Lindley distribution is established. By assuming generalised Weibull and generalised log-logistic baseline distributions, we propose a new class of shared frailty models based on the additive hazard rate. We estimate the parameters in these frailty models and use the Bayesian paradigm of the Markov Chain Monte Carlo (MCMC) technique. Model selection criteria have been applied for the comparison of models. We analyse kidney infection data and suggest the best model.

Keywords

EN

Bayesian estimation; frailty; generalised Lindley frailty; generalised log-logistic distribution; generalised Weibull distribution; hazard rate; MCMC; random censoring

• Publisher: Główny Urząd Statystyczny
• Journal: Statistics in Transition new series
• Year: 2022
• Volume: 23
• Issue: 4
• Pages: 161-176

Dates

published

2022

Contributors

author

Arvind Pandey

• Department of Statistics, Central University of Rajasthan, Rajasthan, India

• https://orcid.org/0000000313246985

author

David D. Hanagal

• Department of Statistics, Savitribai Phule Pune University, Pune-411007, India

• https://orcid.org/0000000313246985

author

Shikhar Tyagi

• Department of Statistics, Central University of Rajasthan, Rajasthan, India

• https://orcid.org/0000000316060844

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Identifiers

DOI

10.2478/stattrans-2022-0048

Biblioteka Nauki

2156991