The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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A Joint Frailty Model for Competing Risks Survival Data

경쟁위험 생존자료에 대한 결합 프레일티모형

  • Ha, Il Do (Department of Statistics, Pukyong National University) ;
  • Cho, Geon-Ho (Faculty of Medical Industry Convergence, Daegu Haany University)
  • 하일도 (부경대학교 통계학과) ;
  • 조건호 (대구한의대학교 의료산업융합학부)
  • Received : 2015.10.22
  • Accepted : 2015.10.26
  • Published : 2015.12.31
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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.

경쟁위험사건들은 다기관 임상시험과 같은 군집화된 임상연구에서 자주 관측되어진다. 본 논문에서는 하나의 군집으로 부터 얻어지는 경쟁위험 생존자료에 대해 공통 프레일티를 허락하는 결합 프레일티모형 접근법을 제안한다. 추론을 위해 어려운 적분 자체를 피하는 다단계 가능도를 사용하여, 대응하는 추론절차를 유도한다. 또한 실제자료 분석을 통해 제안된 방법을 예증한다.

Keywords

References

  1. Cox, D. R. (1972). Regression models and life tables(with discussion), Journal of the Royal Statistical Society B, 34, 187-220.
  2. Duchateau, L. and Janssen, P. (2008). The Frailty Models, Springer, New York.
  3. 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
  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.