Hierarchical likelihood approach for frailty models

Ha, Il Do; Lee, Youngjo; Song, Jae-Kee

doi:10.1093/biomet/88.1.233

Cited by 95 publications

(157 citation statements)

References 16 publications

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“…< y (r) are the ordered distinct event times and λ 0k = λ 0 (y (k) ). Following Ha et al (2001), the h-likelihood for the model (2.1) is given by…”

Section: H-likelihood Proceduresmentioning

confidence: 99%

“…Accordingly, for estimation of (β, v) Ha et al (2001) proposed the use of the profiled h-likelihood h * from which λ 0 in h of (2.3) is eliminated:…”

Section: H-likelihood Proceduresmentioning

confidence: 99%

“…For the inference, the marginal likelihood often involves analytically intractable integrals, particularly when modelling multilevel or correlated frailties. However, the hierarchicallikelihood (h-likelihood; Nelder, 1996, 2001) obviates the need for intractable integration over the frailty terms (Ha et al, 2001Ha and Cho, 2012). It is also important to investigate the potential heterogeneity in event times among clusters (e.g.…”

Section: Introductionmentioning

confidence: 99%

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A visualizing method for investigating individual frailties using frailtyHL R-package

Ha¹,

Noh²

2013

Journal of the Korean Data and Information Science Society

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For analysis of clustered survival data, the inferences of parameters in semi-parametric frailty models have been widely studied. It is also important to investigate the potential heterogeneity in event times among clusters (e.g. centers, patients). For purpose of this analysis, the interval estimation of frailty is useful. In this paper we propose a visualizing method to present confidence intervals of individual frailties across clusters using the frailtyHL R-package, which is implemented from h-likelihood methods for frailty models. The proposed method is demonstrated using two practical examples.

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“…< y (r) are the ordered distinct event times and λ 0k = λ 0 (y (k) ). Following Ha et al (2001), the h-likelihood for the model (2.1) is given by…”

Section: H-likelihood Proceduresmentioning

confidence: 99%

“…Accordingly, for estimation of (β, v) Ha et al (2001) proposed the use of the profiled h-likelihood h * from which λ 0 in h of (2.3) is eliminated:…”

Section: H-likelihood Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A visualizing method for investigating individual frailties using frailtyHL R-package

Ha¹,

Noh²

2013

Journal of the Korean Data and Information Science Society

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show abstract

“…The distribution of frailties u i 's is assumed to follow a gamma distribution with mean E(u i ) = 1 and var (u i ) = α (Hougaard, 2000;Ha et al, 2001). Here, the variance α is called frailty or dispersion parameter.…”

Section: Gamma Frailty Modelsmentioning

confidence: 99%

“…The marginal likelihood approach often requires an intractable integration in order to eliminate the frailties (i.e. random effects) (Ha et al, 2001;Ha and Lee, 2003).…”

mentioning

confidence: 99%

H-likelihood approach for variable selection in gamma frailty models

Cho²

2012

Journal of the Korean Data and Information Science Society

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Recently, variable selection methods using penalized likelihood with a shrink penalty function have been widely studied in various statistical models including generalized linear models and survival models. In particular, they select important variables and estimate coefficients of covariates simultaneously. In this paper, we develop a penalized h-likelihood method for variable selection in gamma frailty models. For this we use the smoothly clipped absolute deviation (SCAD) penalty function, which satisfies a good property in variable selection. The proposed method is illustrated using simulation study and a practical data set.

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Joint Modelling of Repeated Measures and Survival Time Data

Ha¹,

Park

Lee

2003

Biometrical J

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In many clinical trials both repeated measures data and event history data are simultaneously observed from the same subject. These two types of responses are usually correlated, because they are from the same subject. In this article, we propose a joint model for the combined analysis of repeated measures data and event history data in the framework of hierarchical generalized linear models. The correlation between repeated measures and event time is modelled by introducing a shared random effect. The model parameters are estimated using the hierarchical-likelihood approach. The proposed model is illustrated using a real data set for the renal transplant patients.

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Hierarchical likelihood approach for frailty models

Cited by 95 publications

References 16 publications

A visualizing method for investigating individual frailties using frailtyHL R-package

A visualizing method for investigating individual frailties using frailtyHL R-package

H-likelihood approach for variable selection in gamma frailty models

Joint Modelling of Repeated Measures and Survival Time Data

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