DOI QR코드

DOI QR Code

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

  • Ha, Il Do (Department of Asset Management, Daegu Haany University) ;
  • Noh, Maengseok (Department of Statistics, Pukyong National University)
  • Received : 2013.06.03
  • Accepted : 2013.07.07
  • Published : 2013.07.31

Abstract

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.

Keywords

References

  1. Fleming, T. R. and Harrington, D. P. (1991), Counting processes and survival analysis, Wiley, New York.
  2. Ha, I. D. (2008). A HGLM framework for meta-analysis of clinical trials with binary outcome. Journal of the Korean Data & Information Science Society, 19, 1429-1440.
  3. Ha, I. D. and Cho, G. H. (2012). H-likelihood approach for variable selection in gamma frailty models. Journal of the Korean Data & Information Science Society, 23, 190-207. https://doi.org/10.7465/jkdi.2012.23.1.199
  4. 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. https://doi.org/10.1198/1061860032256
  5. Ha, I. D., Lee, Y. and MacKenzie, G. (2007). Model selection for multi-component frailty models. Statistics in Medicine, 26, 4790-4807. https://doi.org/10.1002/sim.2879
  6. Ha, I. D., Lee, Y. and Song, J. K. (2001). Hierarchical likelihood approach for frailty models. Biometrika, 88, 233-243. https://doi.org/10.1093/biomet/88.1.233
  7. Ha, I. D., Noh, M. and Lee, Y. (2012a). frailtyHL: frailty models using h-likelihood. http://CRAN.R-project. org/package=frailtyHL, R package version 1.1, 28-30.
  8. Ha, I. D., Noh, M. and Lee, Y. (2012b). frailtyHL: A package for fitting frailty models with h-likelihood. The R Journal, 4, 28-37.
  9. Ha, I. D., Sylvester, R., Legrand, C. and MacKenzie, G. (2011). Frailty modelling for survival data from multi-centre clinical trials. Statistics in Medicine, 30, 2144-2159. https://doi.org/10.1002/sim.4250
  10. Ha, I. D., Vaida, F. and Lee, Y. (2013). Interval estimation of random effects in proportional hazards models with frailties. Statistical Methods in Medical Research, Published online: 29/January/2013.
  11. Kim, H. K., Noh, M. and Ha, I. D. (2011). A study using HGLM on regional difference of the dead due to injuries. Journal of the Korean Data & Information Science Society, 22, 137-148.
  12. Lee, Y. and Nelder, J. A. (1996). Hierarchical generalized linear models (with discussion). Journal of the Royal Statistical Society B, 58, 619-678.
  13. Lee, Y. and Nelder, J. A. (2001) Hierarchical generalised linear models: A synthesis of generalised linear models, random-effect models and structured dispersions. Biometrika, 88, 987-1006. https://doi.org/10.1093/biomet/88.4.987
  14. Lee, Y. and Nelder, J. A. (2009). Likelihood inference for models with un observables: Another view (with discussion). Statistical Science, 24, 255-293. https://doi.org/10.1214/09-STS277
  15. McGilchrist C.A. and Aisbett, C.W. (1991). Regression with frailty in survival analysis. Biometrics, 47, 461-466. https://doi.org/10.2307/2532138
  16. Noh, M, Ha, I. D. and Lee, Y. (2006). Dispersion frailty models and HGLMs. Statistics in Medicine, 25, 1341-1354. https://doi.org/10.1002/sim.2284
  17. Park, J. K., Oh, K. H. and Kim, M. S. (2012). Survival analysis on the business types of small business using Cox's proportional hazard regression model. Journal of the Korean Data & Information Science Society, 23, 257-269. https://doi.org/10.7465/jkdi.2012.23.2.257
  18. Rondeau, V., Michiels, S., Liquet, B. and Pignon, J. P. (2008). 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, 1894-1910. https://doi.org/10.1002/sim.3161
  19. Therneau, T. M. and Grambsch, P. M. (2000). Modelling survival data: Extending the Cox model, Springer, New York.
  20. Therneau, T. M. (2010). survival: survival analysis, including penalized likelihood. http://CRAN.R-project.org/package=survival, R package version 2.36-2, 28-31.
  21. Vaida, F. and Xu, R. (2000). Proportional hazards model with random effects. Statistics in Medicine, 19, 3309-3324. https://doi.org/10.1002/1097-0258(20001230)19:24<3309::AID-SIM825>3.0.CO;2-9
  22. Yau, K. K. W. (2001). Multilevel models for survival analysis with random effects. Biometrics, 57, 96-102. https://doi.org/10.1111/j.0006-341X.2001.00096.x

Cited by

  1. Visualization and interpretation of cancer data using linked micromap plots vol.25, pp.6, 2014, https://doi.org/10.7465/jkdi.2014.25.6.1531
  2. Joint HGLM approach for repeated measures and survival data vol.27, pp.4, 2016, https://doi.org/10.7465/jkdi.2016.27.4.1083
  3. Statistical analysis of recurrent gap time events with incomplete observation gaps vol.25, pp.2, 2014, https://doi.org/10.7465/jkdi.2014.25.2.327
  4. ML estimation using Poisson HGLM approach in semi-parametric frailty models vol.27, pp.5, 2016, https://doi.org/10.7465/jkdi.2016.27.5.1389
  5. Variable selection in Poisson HGLMs using h-likelihoood vol.26, pp.6, 2015, https://doi.org/10.7465/jkdi.2015.26.6.1513
  6. Analysis of multi-center bladder cancer survival data using variable-selection method of multi-level frailty models vol.27, pp.2, 2016, https://doi.org/10.7465/jkdi.2016.27.2.499