Shared inverse Gaussian frailty models based on additive hazards

Hanagal, David D.; Pandey, Arvind

doi:10.1080/03610926.2016.1260740

Cited by 14 publications

(3 citation statements)

References 40 publications

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“…For kidney infection data, all the covariates have been found statistically significant factors for both models (see Tables 4-5). Our proposed frailty models, Model-I and Model-II, are better as compared to the frailty models by Hanagal et al (2017) and Hanagal and Pandey (2017a) with baseline generalised log-logistic distribution. In a similar way, with a minimum value of AIC, our proposed frailty models are better as compared to the frailty models by Pandey et al (2018).…”

Section: Discussionmentioning

confidence: 92%

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Generalised Lindley shared additive frailty regression model for bivariate survival data

Pandey

Hanagal

Tyagi

2022

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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.

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Section: Discussionmentioning

confidence: 92%

“…Gamma frailty models for bivariate survival data were given by Hanagal and Pandey (2015a). Hanagal and Pandey (2017a) used the shared inverse Gaussian frailty models based on additive hazard. Hanagal (2019) gave an extensive literature review on different shared frailty models.…”

Section: Introductionmentioning

confidence: 99%

Generalised Lindley shared additive frailty regression model for bivariate survival data

Pandey

Hanagal

Tyagi

2022

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“…Hanagal and Pandey (2016) studied the shared frailty models under the assumption that frailty term acting additively to the baseline hazard function by taking gamma distribution as frailty distribution. Hanagal and Pandey (2017) also discussed the additive hazard model associated with the frailty term by considering frailty distributions as gamma and inverse Gaussian under generalized log-logistic, exponential power and generalized Weibull as baseline distributions. In the frailty model, the frailty term V and baseline hazard function r 0 (y) cannot be separated since in a cluster level, the frailty is incorporated with the baseline hazard in a multiplicative manner.…”

Section: Introductionmentioning

confidence: 99%

A Mixture Shared Inverse Gaussian Frailty Model under Modified Weibull Baseline Distribution

Lalpawimawha¹,

Pandey²

2020

AJS

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Frailty models are used in the survival analysis to account for the unobserved heterogeneityin individual risks to disease and death. To analyze the bivariate data on relatedsurvival times (e.g. matched pairs experiments, twin or family data), the shared frailtymodels were suggested. In this manuscript, we propose a new mixture shared inverse Gaussian frailty model based on modified Weibull as baseline distribution. The Bayesian approach of Markov Chain Monte Carlo technique is employed to estimate the parameters involved in the models. In addition, a simulation study is performed to compare the true values of the parameters with the estimated values. A comparison with the existing model was done by using Bayesian comparison techniques. A better model for infectious disease data related to kidney infection is suggested.

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Shared Gamma Frailty Models Based on Reversed Hazard

Hanagal

2019

Modeling Survival Data Using Frailty Models

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Shared inverse Gaussian frailty models based on additive hazards

Cited by 14 publications

References 40 publications

Generalised Lindley shared additive frailty regression model for bivariate survival data

Generalised Lindley shared additive frailty regression model for bivariate survival data

A Mixture Shared Inverse Gaussian Frailty Model under Modified Weibull Baseline Distribution

Shared Gamma Frailty Models Based on Reversed Hazard

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