FrailtyHL: A package for fitting frailty models with h-likelihood

R Journal

Volumn 4, Issue 2, 2012, Pages 28-37

  Ha, Il Do ^a   Noh, Maengseok ^b   Lee, Youngjo ^c  

^a Daegu Haany University   (South Korea)

^b Pukyong National University   (South Korea)

^c Seoul National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84883227108     PISSN: None     EISSN: 20734859     Source Type: Journal    
DOI: 10.32614/rj-2012-010     Document Type: Article

References (36)

1. M. Alam, L. Ronnegard, and X. Shen. hglm: hierarchical generalized linear models, 2010. URL http: //CRAN.R-project.org/package=hglm. R package version 1.1.1. [p28]
2. N. E. Breslow. Discussion of Professor Cox's paper. Journal of the Royal Statistical Society Series B, 34: 216-217, 1972. [p29]
3. N. E. Breslow. Covariance analysis of censored survival data. Biometrics, 30:89-99, 1974. [p30]
4. D. R. Cox and N. Reid. Parameter orthogonality and approximate conditional inference (with discussion). Journal of the Royal Statistical Society Series B, 49:1-39, 1987. [p30]
5. L. Duchateau and P. Janssen. The Frailty Model. Springer-Verlag: New York, 2008. [p28]
6. M. Donohue, R. Overholser, R. Xu, and F. Vaida. Conditional Akaike information under generalized linear and proportional hazards mixed models. Biometrika, 98:685-700, 2011. [p33]
7. M. Donohue and R. Xu. phmm: proportional hazards mixed-effects model, 2012. URL http://CRAN.R-project.org/package=phmm. R package version 0.7-4. [p28, 30, 31]
8. T. R. Fleming and D. P. Harrington. Counting Processes and Survival Analysis.Wiley: New York, 1991. [p29, 33]
9. J. R. Gonzalez, V. Rondeau Y. Mazroui, A. Mauguen, and A. Diakité. frailtypack: frailty models using a semi-parametrical penalized likelihood estimation or aparametrical estimation, 2012. URL http://CRAN.R-project.org/package=frailtypack. R package version 2.2-23. [p28]
10. I. D. Ha and Y. Lee. Estimating frailty models via Poisson hierarchical generalized linear models. Journal of Computational and Graphical Statistics, 12: 663-681, 2003. [p28, 30]
11. I. D. Ha and Y. Lee. Comparison of hierarchical likelihood versus orthodox best linear unbiased predictor approaches for frailty models. Biometrika, 92: 717-723, 2005. [p28, 30]
12. I. D. Ha, Y. Lee and G. MacKenzie. Model selection for multi-component frailty models. Statistics in Medicine, 26:4790-4807, 2007. [p29, 33]
13. I. D. Ha, Y. Lee and J. K. Song. Hierarchical likelihood approach for frailty models. Biometrika, 88: 233-243, 2001. [p28, 29, 30]
14. I. D. Ha, M. Noh and Y. Lee. Bias reduction of likelihood estimators in semi-parametric frailty models. Scandinavian Journal of Statistics, 37:307-320, 2010. [p30, 31, 32]
15. I. D. Ha, M. Noh and Y. Lee. frailtyHL: frailty models using h-likelihood, 2012. URL http://CRAN.R-project.org/package=frailtyHL. R package version 1.1. [p28, 30]
16. I. D. Ha, T. Park and Y. Lee. Joint modelling of repeated measures and survival time data. Biometrical Journal, 45:647-658, 2003. [p32]
17. I. D. Ha, R. Sylvester, C. Legrand and G. MacKenzie. Frailty modelling for survival data from multicentre clinical trials. Statistics in Medicine, 30:2144-2159, 2011. [p31, 32]
18. P. Hougaard. Analysis of Multivariate Survival Data. Springer-Verlag: New York, 2000. [p28, 29]
19. Y. Lee and I. D. Ha. Orthodox BLUP versus hlikelihood methods for inferences about random effects in Tweedie mixed models. Statistics and Computing, 20:295-303, 2010. [p31]
20. Y. Lee, M. Jang and W. Lee. Prediction interval for disease mapping using hierarchical likelihood. Computational Statistics, 26:159-179, 2011. [p31]
21. Y. Lee and J. A. Nelder. Hierarchical generalized linear models (with discussion). Journal of the Royal Statistical Society Series B, 58:619-678, 1996. [p28, 30]
22. Y. Lee and J. A. Nelder. Hierarchical generalised linear models: a synthesis of generalised linear models, random-effect models and structured dispersions. Biometrika, 88:987-1006, 2001. [p30]
23. Y. Lee and J. A. Nelder. Double hierarchical generalised linear models (with discussion). Applied Statistics, 55:139-185, 2006. [p32]
24. Y. Lee, J. A. Nelder and Y. Pawitan. Generalised Linear Models with Random Effects: Unified Analysis via Hlikelihood. Chapman & Hall: London, 2006. [p28, 30, 31, 32, 33, 35]
25. N. Mantel, N. R. Bohidar and J. L. Ciminera. Mantel-Haenszel analyses of litter-matched timeto- response data, with modifications for recovery of interlitter information. Cancer Research, 37:3863-3868, 1977. [p32]
26. C. A. McGilchrist and C.W. Aisbett. Regression with frailty in survival analysis. Biometrics, 47:461-466, 1991. [p31]
27. M. Molas. HGLMMM: hierarchical generalized linear models, 2010. URL http://CRAN.R-project.org/package=HGLMMM. R package version 0.1.1. [p28]
28. M. Noh and Y. Lee. dhglm: double hierarchical generalized linear models, 2011. URL http://CRAN.R-project.org/package=dhglm. R package version 1.0. [p28]
29. S. Ripatti and J. Palmgren. Estimation of multivariate frailty models using penalized partial likelihood. Biometrics, 56:1016-1022, 2000. [p30]
30. V. Rondeau, S. Michiels, B. Liquet and J. P. Pignon. Investigating trial and treatment heterogeneity in an individual patient data meta-analysis of survival data by means of the penalized maximum likelihood approach. Statistics in Medicine, 27:1894-910, 2008. [p35]
31. Y. Sakamoto, M. Ishiguro and G. Kitagawa. Akaike Information Criterion Statistics. KTK Scientific Publisher: Tokyo, 1986. [p33]
32. T. M. Therneau. survival: survival analysis, including penalised likelihood, 2010. URL http://CRAN.R-project.org/package=survival. R package version 2.36-2. [p28, 30, 31]
33. T. M. Therneau. coxme: Mixed Effects Cox Models, 2011. URL http://CRAN.R-project.org/package=coxme. R package version 2.2-1. [p28, 30]
34. T. M. Therneau and P. M. Grambsch. Modelling Survival Data: Extending the Cox Model. Springer- Verlag: New York, 2000. [p29, 30, 31]
35. F. Vaida and R. Xu. Proportional hazards model with random effects. Statistics in Medicine, 19:3309-3324, 2000. [p32, 35]
36. K. K.W. Yau. Multilevel models for survival analysis with random effects. Biometrics, 57:96-102, 2001. [p29]