frailtyHL-package {frailtyHL}

H-likelihood Approach for Frailty Models

Description

The frailtyHL package fits frailty models which are Cox's proportional hazards models incorporating random effects. The function implements the h-likelihood estimation procedures. For the frailty distribution lognormal and gamma are allowed. The h-likelihood uses the Laplace approximation when the numerical integration is intractable, giving a statistically efficient estimation in frailty models. (Ha, Lee and Song, 2001; Ha and Lee, 2003, 2005; Lee, Nelder and Pawitan, 2017; Ha, Jeong and Lee, 2017). This package handles various random-effect survival models such as time-dependent frailties, competing-risk frailty models, AFT random-effect models, and joint modelling of linear mixed models and frailty models. It also provides penalized variable-selection procedures (LASSO, SCAD and HL).

Details

Package:	frailtyHL
Type:	Package
Version:	2.1
Date:	2016-09-19
License:	Unlimited
LazyLoad:	yes

This is version 2.2 of the frailtyHL package.

Author(s)

Il Do Ha, Maengseok Noh, Jiwoong Kim, Youngjo Lee

Maintainer: Maengseok Noh <[e-mail]>

References

Ha, I. D. and Lee, Y. (2003). Estimating frailty models via Poisson Hierarchical generalized linear models. Journal of Computational and Graphical Statistics, 12, 663-681.

Ha, I. D. and Lee, Y. (2005). Comparison of hierarchical likelihood versus orthodox best linear unbiased predictor approaches for frailty models. Biometrika, 92, 717-723.

Ha, I. D., Lee, Y. and Song, J. K. (2001). Hierarchical likelihood approach for frailty models. Biometrika, 88, 233-243.

Ha, I. D., Jeong, J. and Lee, Y. (2017). Statistical modelling of survival data with random effects. Springer.

Lee, Y., Nelder, J. A. and Pawitan, Y. (2017). Generalised linear models with random effects: unified analysis via h-likelihood. 2nd Edition. Chapman and Hall: London.

Examples

data(kidney)
kidney_g12<-frailtyHL(Surv(time,status)~sex+age+(1|id),kidney)