A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence

Clayton, David

doi:10.1093/biomet/65.1.141

Cited by 1,688 publications

(774 citation statements)

References 12 publications

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“…Individuals in a cluster are assumed to share the same frailty, which is why this model is called shared frailty model. It was introduced by (Clayton, 1978) and extensively studied in (Hougaard , 2000). Therefore, in this study, there may be similar frailty with in subcategories of fistula patients, based on living geographical area of women specifically zone.And the model best fit the data were selected using Akaike information criteria (AIC) and Bayesian information criteria(BIC).…”

Section: Introductionmentioning

confidence: 84%

“…It assumes that, the given frailty, all event times in a cluster are independent. Shared frailty model was introduced by Clayton (1978) without using the notion frailty and extensively studied by (Hougaard, Therneau &Grambsch,2000, andDuchateau et al,2003).It assumes that similar observations share frailty, even though frailty may vary from group to group. In effect, shared frailty causes observations within the same group to be correlated.…”

Section: Shared Frailty Modelmentioning

confidence: 99%

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Determinants and Heterogeneity of Time -to- Recovery from Obstetric Fistula Patients; Comparison of Acceleration Failure Time and Parametric Shared Frailty Models.

Demissie¹

2017

IJSP

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Obstetric Fistula is a medical condition that involves an opening or perforation between the vagina and the bladder or the vagina and the rectum. It is a serious, life threatening and often debilitating medical condition that affects thousands of women in developing countries.The study consists of 270 obstetric fistula patients having all required information who were taking treatment at Jimma University Specialized Hospital in south west Ethiopia from January, 2011 to January ,2017. The log-rank and generalized wilcoxon test were used to explore the association between the recovery time and different independent categorical covariates. Then using different baseline distribution parametric models were employed to have an appropriate model for the recovery time/status of the patients based on Akaike information criteria (AIC) of the model. Result of Both log-rank and generalized wilcoxon test showed that there were significant differences among obstetric fistula patients in survival experience of weight , marital status, Residence, Incontinence, Antenatal care, mode of delivery, status of urethra and types of fistula of patients at 5% level of significance. Based on AIC log-normal gamma shared frailty model is an appropriate model and there is heterogeneity between patients with zone. The final model showed that marital status, Residence, Incontinence, Antenatal care, status of urethra and types of fistula were the determinants of recovery status of the patients at 5% level of significance. In Conclusion, the result showed that married women, rural residence, incontinence of urine more than 3 months, hadn't antenatal care and completely damaged urethra were prolonged time recovery time of patients whereas having recto-vaginal-fistula shorten recovery status than vesico-vaginal fistula group of patients.

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Section: Introductionmentioning

confidence: 84%

Section: Shared Frailty Modelmentioning

confidence: 99%

Determinants and Heterogeneity of Time -to- Recovery from Obstetric Fistula Patients; Comparison of Acceleration Failure Time and Parametric Shared Frailty Models.

Demissie¹

2017

IJSP

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“…Fixed-frailty models can be extended to capture such phenomena. Shared frailty models (introduced by Clayton 1978) are based on the assumption that a heterogeneous population is stratified, and all individuals within a cluster share the same frailty.…”

Section: Resultsmentioning

confidence: 99%

Unobserved population heterogeneity

Vaupel¹,

Missov²

2014

DemRes

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“…While some authors use continuous distributions such as Gamma (Clayton, 1978;Vaupel et al, 1979), inverse Gaussian (Hougaard, 1984;Whitmore & Lee, 1991;Hanagal & Sharma, 2015), log-normal (McGilchrist & Aisbett, 1991) and positive stable (Hougaard, 1986), other authors use discrete distributions (Caroni, Crowder, & Kimber, 2010;Ata & Ozel, 2012). However, the Gamma distribution is the most common and widely used in literature for determining the frailty effect, which acts multiplicatively on the baseline hazard (Wienke, 2011).…”

Section: Shared Frailty Modelsmentioning

confidence: 99%

“…They indicated that the individual hazard is the product of two terms: an individual level frailty and a baseline hazard function. The multivariate generalization was then introduced by Clayton (1978). The proposed model was a random effect model, which is an extension of a proportional hazards (PH) approach.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Shared Frailty Models Based on Particle Swarm Optimization

Aşkın¹,

İnan²,

Büyüklü³

2016

IJSP

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Standard survival techniques such as proportional hazards model are suffering from the unobserved heterogeneity. Frailty models provide an alternative way in order to account for heterogeneity caused by unobservable risk factors. Although vast studies have been done on estimation procedures, Evolutionary Algorithms (EAs) haven't received much attention in frailty studies. In this paper, we investigate the estimation performance of maximum likelihood estimation (MLE) via Particle Swarm Optimization (PSO) in modelling multivariate survival data with shared gamma frailty. Simulation studies and real data application are performed in order to assess the performance of MLE via PSO, quasi-Newton and conjugate gradient method.

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A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence

Cited by 1,688 publications

References 12 publications

Determinants and Heterogeneity of Time -to- Recovery from Obstetric Fistula Patients; Comparison of Acceleration Failure Time and Parametric Shared Frailty Models.

Determinants and Heterogeneity of Time -to- Recovery from Obstetric Fistula Patients; Comparison of Acceleration Failure Time and Parametric Shared Frailty Models.

Unobserved population heterogeneity

Parameter Estimation of Shared Frailty Models Based on Particle Swarm Optimization

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