Comparison of Shared Frailty Models for Kidney Infection Data under Exponential Power Baseline Distribution

Hanagal, David D.; Dabade, Alok D.

doi:10.1080/03610926.2013.813045

Cited by 12 publications

(3 citation statements)

References 14 publications

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“…These observations agree with those in the literature (e.g. McGilchrist & Aisbett, ; Yu, ; Hanagal & Dabade, ). Moreover, all the covariates have around double strength of effects on the log hazard of the second outcome T 2 compared with the first outcome T 1 .…”

Section: Discussionsupporting

confidence: 93%

Collective versus Individual Effects in Survival Analysis of Multiple Failures

Huang

et al. 2015

Scandinavian J Statistics

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In survival analysis, we sometimes encounter data with multiple censored outcomes. Under certain scenarios, partial or even all covariates have 'similar' relative risks on the multiple outcomes in the Cox regression analysis. The similarity in covariate effects can be quantified using the proportionality of regression coefficients. Identifying the proportionality structure, or equivalently whether covariates have individual or collective effects, may have important scientific implications. In addition, it can lead to a smaller set of unknown parameters, which in turn results in more accurate estimation. In this article, we develop a novel approach for identifying the proportionality structure. Simulation shows the satisfactory performance of the proposed approach and its advantage over estimation under no assumed structure. We analyse three datasets to demonstrate the practical application of the proposed approach.

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Section: Discussionsupporting

confidence: 93%

Collective versus Individual Effects in Survival Analysis of Multiple Failures

Huang

et al. 2015

Scandinavian J Statistics

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“…Gompertz, log-logistic, and generalized exponential, have been explored. The distributions are widely applicable in survival studies for different types of datasets and have been modelled with a gamma frailty, inverse Gaussian frailty, and log-normal frailty (Manton et al, 1986;Klein et al, 1999;Wienke et al, 2003Wienke et al, , 2005Locatelli et al, 2004;Hens et al, 2009;Hanagal and Dabade, 2013;Hanagal and Sharma, 2013) among other distributions, but have not yet been used in conjunction with the generalized gamma frailty effect.…”

Section: Introductionmentioning

confidence: 99%

The Generalized Gamma Shared Frailty Model under Different Baseline Distributions

Sidhu

Jain

Sharma

2019

Int J Math, Eng, Manag Sci

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In the analysis of clustered survival data, shared frailty models are often used when observations in the same group share common unknown risk factors or frailty. There is dependence in the event times belonging to the same group, while event times from different groups are conditionally independent given their covariates. In such models, the known effect on survival time is described using the baseline distribution and regression coefficients while the unknown effect is described through a frailty distribution. In this paper, the Gompertz, log-logistic, and generalized exponential distributions are studied as baseline distributions, under a shared frailty effect described by the generalized gamma distribution. Their hazard functions have been compared and their applicability under different settings and performance with generalized gamma frailty has been explored. These models are fitted to three real life datasets using Bayesian estimation methods and compared using the Bayesian Information Criteria (AIC, BIC, and DIC) and the Bayes Factor.

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“…The EP distribution has been discussed by many authors, for examples, Leemis (1986), Rajarshi & Rajarshi (1988), Hanagal & Dabade (2015), among others. Moreover, exponential power distribution is not only used by survival analysis but is also related with asymmetrical exponential power distributions in statistics as mentioned in Hazan et al (2003) and Delicado & Goria (2008 In recent years, many researchers have discussed statistical inference about the parameter in the EP model from different perspectives.…”

Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Estimation of Parameters in Exponential Power Distribution with Upper Record Values

Zhi¹

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Comparison of Shared Frailty Models for Kidney Infection Data under Exponential Power Baseline Distribution

Cited by 12 publications

References 14 publications

Collective versus Individual Effects in Survival Analysis of Multiple Failures

Collective versus Individual Effects in Survival Analysis of Multiple Failures

The Generalized Gamma Shared Frailty Model under Different Baseline Distributions

Maximum Likelihood Estimation of Parameters in Exponential Power Distribution with Upper Record Values

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