2021/12/21 by Shikhar Tyagi, Tyagi, Shikhar, Arvind Pandey +4
Economics, Econometrics and Finance · Mathematics · Medicine · Social Sciences · #62-08 #62D10 #62D20 #62R07 #62T09 #A.0 #Applications (stat.AP) #B.0 #Bayesian probability #Computer science #Context (archaeology) #Covariate #D.3 #E.0 #Econometrics #F.2 #FOS: Computer and information sciences #Frailty in Older Adults #G.3 #Geography #H.1 #Health Systems, Economic Evaluations, Quality of Life #I.6 #Insurance, Mortality, Demography, Risk Management #Markov chain Monte Carlo #Mathematics #Methodology (stat.ME) #Model selection #Proportional hazards model #Statistics #Weibull distribution #acm:62-08 #acm:62D10 #acm:62D20 #acm:62R07 #acm:62T09 #msc:62-08 #msc:62D10 #msc:62D20 #msc:62R07 #msc:62T09 #stat.AP #stat.ME
paper · pdf · doi:10.48550/arxiv.2112.10986
published in arXiv (Cornell University) (Cornell University) · 28 pages,3 figures,18 tables
arxiv created 2021/12/21 · openalex publication_date 2021/12/21 · arxiv updated 2021/12/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Traditional survival analysis techniques focus on the occurrence of failures over the time. During analysis of such events, ignoring the related unobserved covariates or heterogeneity involved in data sample may leads us to adverse consequences. In this context, frailty models are the viable choice to investigate the effect of the unobserved covariates. In this article, we assume that frailty acts multiplicatively to hazard rate. We propose inverse Gaussian (IG) and generalized Lindley (GL) shared frailty models with generalized Weibull (GW) as baseline distribution in order to analyze the unobserved heterogeneity. To estimate the parameters in models, Bayesian paradigm of Markov Chain Monte Carlo technique has been proposed. Model selection criteria have been used for the comparison of models. Three different cancer data sets have been analyzed using the shared frailty models. Better models have been suggested for the data sets.