Estimating Frailty Models via Poisson Hierarchical Generalized Linear Models

Ha, Il Do; Lee, Youngjo

doi:10.1198/1061860032256

Cited by 60 publications

(106 citation statements)

References 23 publications

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“…where τ = τ (θ) = ( β T (θ), v T (θ)) T and J(h * ; τ ) = −∂ 2 h * /∂τ 2 is an information matrix for τ with a detailed form in (3.1): see also Ha and Lee (2003). The outline of frailtyHL() package (Ha et al, 2012b) for fitting frailty model (2.1) is below.…”

Section: H-likelihood Proceduresmentioning

confidence: 99%

“…In frailty models (2.1), as in HGLMs (Lee and Nelder, 2009;Kim et al, 2011), location parameters (β, λ 0 , v) and frailty parameters θ are asymptotically orthogonal (Ha and Lee, 2003;Ha et al, 2011). For a moment, assume that θ is known.…”

Section: Interval Estimation Of Frailty and R-codesmentioning

confidence: 99%

“…by using h * . Following Ha and Lee (2003) and Ha (2008), the asymptotic covariances forβ andv − v are obtained from the inverse of information matrix, J(h * ; β, v) = −∂ 2 h * /∂(β, v) 2 , of β and v based on h * :…”

Section: Interval Estimation Of Frailty and R-codesmentioning

confidence: 99%

“…where X and Z are the n × p and n × q model matrices for β and v whose ijth row vectors are x T ij and z T ij , respectively, U = −∂ 2 2 /∂v 2 , and the weight matrix W * = W * (β, v) is given in Appendix B of Ha and Lee (2003). This means that an approximated variance ofv − v can be computed from the lower right-hand corner of J −1 in (3.1), leading to…”

Section: Interval Estimation Of Frailty and R-codesmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: H-likelihood Proceduresmentioning

confidence: 99%

Section: Interval Estimation Of Frailty and R-codesmentioning

confidence: 99%

Section: Interval Estimation Of Frailty and R-codesmentioning

confidence: 99%

Section: Interval Estimation Of Frailty and R-codesmentioning

confidence: 99%

See 2 more Smart Citations

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 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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Estimating Frailty Models via Poisson Hierarchical Generalized Linear Models

Cited by 60 publications

References 23 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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