Description Details Author(s) References Examples
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).
Package: | frailtyHL |
Type: | Package |
Version: | 2.1 |
Date: | 2016-09-19 |
License: | Unlimited |
LazyLoad: | yes |
This is version 2.2 of the frailtyHL package.
Il Do Ha, Maengseok Noh, Jiwoong Kim, Youngjo Lee
Maintainer: Maengseok Noh <msnoh@pknu.ac.kr>
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.
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Loading required package: Matrix
Loading required package: survival
Loading required package: cmprsk
iteration :
46
convergence :
7.61095e-07
[1] "converged"
[1] "Results from the log-normal frailty model"
[1] "Number of data : "
[1] 76
[1] "Number of event : "
[1] 58
[1] "Model for conditional hazard : "
Surv(time, status) ~ sex + age + (1 | id)
[1] "Method : HL(0,1)"
[1] "Estimates from the mean model"
Estimate Std. Error t-value p-value
sex -1.380431 0.43082 -3.2042 0.001354
age 0.004885 0.01209 0.4041 0.686123
[1] "Estimates from the dispersion model"
Estimate Std. Error
id 0.5345 0.3384
-2h0 -2*hp -2*p_b,v(hp)
[1,] 330.4 390.77 371.54
cAIC pAIC rAIC
[1,] 362.46 370.7 373.54
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