Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
권호 표지
DOI QR코드

DOI QR Code

직장암 데이터에 대한 위험률 함수 추정 및 위험률 변화점 추정

Estimation of hazard function and hazard change-point for the rectal cancer data

  • 이시은 (덕성여자대학교 정보통계학과) ;
  • 심병용 (가톨릭대학교 성빈센트병원) ;
  • 김재희 (덕성여자대학교 정보통계학과)
  • Lee, Sieun (Department of Information and Statistics, Duksung Women's University) ;
  • Shim, Byoung Yong (The Catholic University of Korea St. Vincent's Hospital) ;
  • Kim, Jaehee (Department of Information and Statistics, Duksung Women's University)
  • 투고 : 2015.07.10
  • 심사 : 2015.09.16
  • 발행 : 2015.11.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구에서는 직장암 환자들의 수술 후 재발까지의 시간 데이터에 대해 집단 간 생존함수 양상에 차이가 있는지 로그 순위 검정 결과 유의수준 10%에서 포도당 단일수송체 (GLUT1)의 수준, 수술 전 병기 (cstage), 수술 후 병기 (ypstage)에 따른 차이가 유의하며, Cox 비례위험률 모형을 이용하여 검정한 결과 가장 유의한 공변량은 포도당 단일수송체와 수술 후 병기였다. 지수분포를 따른다고 가정할 경우, 우도함수를 기반한 여러 가지 위험률 변화점을 추정하였다.

In this research, we fit various survival models and conduct tests and estimation for the hazard change-point with the rectal cancer data. By the log-rank tests, at significance level ${\alpha}=0.10$, survival functions are significantly different according to the uniporter of glucose (GLUT1), clinical stage (cstage) and pathologic stage (ypstage). From the Cox proportional hazard model, the most significant covariates are GLUT1 and ypstage. Assuming that the rectal cancer data follows the exponential distribution, we estimate one hazard change-point using Matthews and Farewell (1982), Henderson (1990) and Loader (1991) methods.

키워드

참고문헌

  1. Ahn, J. E. (2010). Statistical analysis of biomedical data using SPSS 18.0, Hannarae Publishing, Seoul.
  2. Collectt, D. (1994). Modelling Survival Data in Medical Research. Chapman & Hall, New York.
  3. Cox, D. R. and Snell, E. J. (1968). A general definition of residuals. Journal of the Royal Statistical Society. Series B (Methodological), 30, 248-275.
  4. Fleming, T. R. and Harrington, D. P. (1991). Counting processes and survival analysis. Wiley, New York.
  5. Han, M. H. and Jeong, K. M. (1998). Estimation for change-point model of hazard rate. Korean Communications in Statistics, 477-489.
  6. Henderson, R. (1990). A problem with the likelihood ratio test for a change-point hazard rate model. Biometrika, 77, 835-843. https://doi.org/10.1093/biomet/77.4.835
  7. James, B., James, K. L. and Siegmund, D. (1987). Tests for a change-point, Biometrika, 74, 71-83. https://doi.org/10.1093/biomet/74.1.71
  8. Jung, S. W., Park, J. Y., Kiim, Y. S., Jeen, Y. T., Lee, H. S., Chun, H. J., Um, S. H., Lee, S. W., Choi, J. H., Kim, C. D., Ryu, H. S. and Hyun, J. H. (2005). Survival analysis according to treatment modality in pancreatic cancer patients. Korean Journal of Gastroenterology, 46, 120-128.
  9. Kim, J. (2009). A change-point estimator with the hazard ratio. Journal of the Korean Statistical Society, 38, 377-382. https://doi.org/10.1016/j.jkss.2009.02.004
  10. Kim, Y. J. (2013). Survival analysis, Free Academy, Seoul.
  11. Klein, J. P. and Moeschberger, M. L. (1997). Survival analysis, Springer, New York.
  12. Loader, C. R. (1991). Inference for a hazard rate change point. Biometrika, 78, 749-757. https://doi.org/10.1093/biomet/78.4.749
  13. Maas, M., Nelemans, P. J., Valentini, V., Das, P., Rodel, C., Kuo, L.-J., Calvo, F. A., Garcia-Aguilar, J., Glynne-Jones, R., Haustermans, K., Mohiuddin, M., Pucciarelli, S., William Small, W. Jr., Suarez, J., Theodoropoulos, G., Biondo, S., Beets-Tan, R. G. and Beets, G. L. (2010). Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: A pooled analysis of individual patient data. Lancet Oncology, 11, 835-844. https://doi.org/10.1016/S1470-2045(10)70172-8
  14. Matthews, D. E. and Farewell, V. T. (1982). On testing for constant hazard against a change-point alternative. Biometrics, 38, 463-468. https://doi.org/10.2307/2530460
  15. Nguyen, H. T., Rogers, G. S. and Walker, E. A. (1984). Estimation in change-point hazard rate models. Biometrika, 71, 299-304. https://doi.org/10.1093/biomet/71.2.299
  16. Oh, J. S. and Lee, S. Y. (2014). An extension of multifactor dimensionality reduction method for detecting gene-gene interactions with the survival time. Journal of the Korean Data & Information Science Society, 25, 1057-1067. https://doi.org/10.7465/jkdi.2014.25.5.1057
  17. Pettit, A. N. (1980). A simple cumulative sum type statistic for the change point problem with zero-one observations. Biometrika, 67, 79-84. https://doi.org/10.1093/biomet/67.1.79
  18. Seo, M. R., Lee, E. B., Yang, W. S., Kim, S. B., Park, S. K., Lee, S. G., Park, J. S. and Hong, C. G. (2002). Survival analysis of hemodialysis patients - A single center study. Korean Society of Nephrology, 21, 636-644.
  19. Shim, B. Y., Jung, J. H., Lee, K. M., Kim, H. J., Hong, S. H., Kim, S. H., Sun, D. S. and Cho, H. M. (2013). Glucose transporter 1 (GLUT1) of anaerobic glycolysis as laparoscopic surgery for locally advanced rectal cancer. International Journal of Colorectal Disease, 28, 375-383. https://doi.org/10.1007/s00384-012-1542-3
  20. Shin, S. B. and Kim, Y. J. (2014). Statistical analysis of recurrent gap time events with incomplete observation gaps. Journal of the Korean Data & Information Science Society, 25, 327-336. https://doi.org/10.7465/jkdi.2014.25.2.327
  21. Worsley, K. J. (1988). Exact percentage points of the likelihood-ratio test for a change-point hazard-rate model. Biometrika, 44, 259-263.
  22. Yao, Y. C. (1986). Maximum likelihood estimation in hazard rate models with change-point. Communications in Statistics A, 15, 2455-2466. https://doi.org/10.1080/03610928608829261
  23. Zhang, W., Qian, L. and Li, Y. (2014). Semiparametric sequential testing for multiple change points in piecewise constant hazard functions with long-term survivors. Communications in Statistics - Simulation and Computation, 43, 1685-1699. https://doi.org/10.1080/03610918.2012.742106

피인용 문헌

  1. Comparison of parametric and nonparametric hazard change-point estimators vol.27, pp.5, 2016, https://doi.org/10.7465/jkdi.2016.27.5.1253
  2. Nonparametric estimation of the discontinuous variance function using adjusted residuals vol.27, pp.1, 2016, https://doi.org/10.7465/jkdi.2016.27.1.111
  3. Cox 비례위험모형을 이용한 우측 대장암 3기 자료 분석 vol.28, pp.2, 2017, https://doi.org/10.7465/jkdi.2017.28.2.349
  4. A Bayesian time series model with multiple structural change-points for electricity data vol.28, pp.4, 2015, https://doi.org/10.7465/jkdi.2017.28.4.889
  5. 위암등록자료에 대한 프레일티 모형 적합 vol.29, pp.4, 2015, https://doi.org/10.7465/jkdi.2018.29.4.1037