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A Joint Frailty Model for Competing Risks Survival Data

경쟁위험 생존자료에 대한 결합 프레일티모형

Ha, Il Do;Cho, Geon-Ho
하일도;조건호

  • Received : 2015.10.22
  • Accepted : 2015.10.26
  • Published : 2015.12.31

Abstract

Competing-risks events are often observed in a clustered clinical study such as a multi-center clinical trial. We propose a joint modelling approach via a shared frailty term for competing risks survival data from a cluster. For the inference we use the hierarchical likelihood (or h-likelihood), which avoids an intractable integration. We derive the corresponding h-likelihood procedure. The proposed method is illustrated via the analysis of a practical data set.

Keywords

competing risks models;frailty models;h-likelihood;joint models;random effects

References

  1. Fine, J. P. and Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk, Journal of the American Statistical Association, 94, 496-509. https://doi.org/10.1080/01621459.1999.10474144
  2. Cox, D. R. (1972). Regression models and life tables(with discussion), Journal of the Royal Statistical Society B, 34, 187-220.
  3. Duchateau, L. and Janssen, P. (2008). The Frailty Models, Springer, New York.
  4. Fine, J. P., Jiang, H. and Chappell, R. (2001). On semi-competing risks data, Biometrika, 88, 907-919. https://doi.org/10.1093/biomet/88.4.907
  5. Fisher, B., Dignam, J., Bryant, J., DeCillis, A., Wickerham, D. L., Wolmark, N., Costantino, J., Redmond, C., Fisher, E. R., Bowman, D. M., Deschenes, L., Dimitrov, N. V., Margolese, R. G., Robidoux, A., Shibata, H., Terz, J., Paterson, A. H. G., Feldman, M. I., Farrar, W., Evans, J. and Lickley, H. L. (1996). Five versus more than five years of tamoxifen therapy for breast cancer patients with negative lymph nodes and estrogen receptor-positive tumors, Journal of the National Cancer Institute, 88, 1529-1542. https://doi.org/10.1093/jnci/88.21.1529
  6. Gorfine, M. and Hsu, L. (2011). Frailty-based competing risks model for multivariate survival data, Biometrics, 67, 415-426. https://doi.org/10.1111/j.1541-0420.2010.01470.x
  7. Ha, I. D., Christian, N. J., Jeong, J.-H., Park, J. and Lee, Y. (2014). Analysis of clustered competing risks data using subdistribution hazard models with multivariate frailties, Statistical Methods in Medical Research, Published online: 11/March/2014.
  8. 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
  9. Ha, I. D., Lee, Y. and MacKenzie, G. (2007). Model selection for multi-component frailty models, Statistics in Medicine, 22, 4790-4807.
  10. 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
  11. Ha, I. D., Noh, M. and Lee, Y. (2012). frailtyHL: A package for fitting frailty models with h-likelihood, The R Journal, 4, 307-320.
  12. 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, 28-37.
  13. 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.
  14. Hougaard, P. (2000). Analysis of Multivariate Survival Data, Springer, New York.
  15. Huang, X. and Wolfe, R. (2002). A frailty model for informative censoring, Biometrics, 58, 510-520. https://doi.org/10.1111/j.0006-341X.2002.00510.x
  16. Lee, Y. and Nelder, J. A. (1996). Hierarchical generalized linear models (with discussion), Journal of the Royal Statistical Society B, 58, 619-678.
  17. Lee, Y., Nelder, J. A. and Pawitan, Y. (2006). Generalised Linear Models with Random Effects: Unified Analysis via h-Likelihood, Chapman and Hall, London.
  18. Pintilie, M. (2006). Competing Risks: A Practical Perspective, Wiley, Chichester.
  19. Prentice, R., Kalbeisch, J. D., Peterson, A. V., Flournoy, N., Farewell, V. T. and Breslow, N. E. (1978). The analysis of failure times in the presence of competing risks, Biometrics, 34, 541-554. https://doi.org/10.2307/2530374
  20. Putter, H., Fiocco, M. and Geskus, R. B. (2007). Tutorial in biostatistics: Competing risks and multi-state models, Statistics in Medicine, 26, 2389-2430. https://doi.org/10.1002/sim.2712
  21. Therneau, T. M. and Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model, Springer, New York.