Correlated Positive Stable Frailty Models Based on Reversed Hazard Rate

David D. Hanagal ()
Additional contact information
David D. Hanagal: Savitribai Phule Pune University

Statistics in Biosciences, 2022, vol. 14, issue 1, No 3, 42-65

Abstract: Abstract Frailty models are used in the survival analysis to account for the unobserved heterogeneity in individual risks to disease and death. To analyze the bivariate data on related survival times (e.g., matched pairs’ experiments, twin, or family data), the shared frailty models were suggested. Shared frailty models are used despite their limitations. To overcome their disadvantages, correlated frailty models may be used. In this paper, we introduce the correlated positive stable frailty models based on reversed hazard rate with three different baseline distributions namely, the generalized log-logistic type I, the generalized log-logistic type II, and the modified inverse Weibull. We introduce the Bayesian estimation procedure using Markov-Chain Monte-Carlo (MCMC) technique to estimate the parameters involved in these models. We present a simulation study to compare the true values of the parameters with the estimated values. We also apply the proposed models to the Australian twin dataset, and a better model is suggested.

Keywords: Australian twin data; Bayesian estimation; Correlated positive stable frailty; Generalized log-logistic distribution; Modified inverse Weibull distribution; 62F15; 62N01; 62P10 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12561-021-09313-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stabio:v:14:y:2022:i:1:d:10.1007_s12561-021-09313-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12561

DOI: 10.1007/s12561-021-09313-7

Access Statistics for this article

Statistics in Biosciences is currently edited by Hongyu Zhao and Xihong Lin

More articles in Statistics in Biosciences from Springer, International Chinese Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().