응용통계연구 (The Korean Journal of Applied Statistics)

  • 제15권2호
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  • Pages.233-250
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  • 2002
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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잔차에 기초한 비례위험모형의 회귀진단법 고찰 - PBC 자료를 통한 응용 연구

Review on proportional hazards regression diagnostics based on residuas

  • 이성임 (서울대학교 복잡계통계연구센터) ;
  • 박성현 (서울대학교 통계학과)
  • 발행 : 2002.09.01
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초록

Cox의 비례위험모형(proportional hazards model)은 생존자료(survival data)에 대한 회귀모형으로 경제학 및 의·공학을 비롯한 여러 응용 분야에서 가장 널리 쓰이고 있는 모형 중 하나이다. 그러나, 이 모형은 일반선헝모형에 비해 잔차 분석을 통한 회귀 진단의 연구가 널리 알려져 있지 않아, 국내의 실제 자료 분석에서는 잔차 분석에 대한 활용이 거의 이루어지지 않고 있는 실정이다. 이에 본 논문에서는 그 동안 제안된 여러 잔차들을 비교 분석하고, S-plus 프로그램을 이용한 PBC(primary biliary cirrhosis) 자료분석을 통해 각 잔차들의 의미를 고찰하고자 한다.

Cox's proportional hazard model is highly-used for the regression analysis of survival data in various fields. Regression diagnostics for the proportional hazards model, however, is not as well-known as the diagnostics for the classical linear models and so these diagnostic methods are not used widely in our practical data analyses. For this reason, we review the residuals proposed by several authors, and investigate how to use them in assessing the model. We also provide the results and interpretation with the analysis of PBC data using S-plus 2000 program.

키워드

참고문헌

  1. Journal of the Korean Statistical Society v.24 Goodness of fit tests of Cox's proportional hazards model 송혜향;이선호
  2. 응용통계연구 v.10 비례위험모형의 적합도 검정에 관한 연구 장애방;이재원
  3. Ph.D.dissertation, University of California Statistical inference for a family of counting processes Aalen, O. O.
  4. The Annals of Statistics v.10 Cox's regression model for counting processes: A large sample study Andersen, P.K.;Gill, R.D. https://doi.org/10.1214/aos/1176345976
  5. Statistical Models Based on Counting Processes Andersen, P.K.;Borgan, O.;Gill, R.D.;Keiding, N.
  6. Biometrika v.75 Residuals for relative risk regression Barlow, W.E.;Prentice, R.L. https://doi.org/10.1093/biomet/75.1.65
  7. Regression Diagnostics Belsley, D.A.;Kuh, E.;Welsch, R.E.
  8. Biometrics v.30 Covariance analysis of censored survival data Breslow, N.E. https://doi.org/10.2307/2529620
  9. Biometrics v.40 Approximate case influence for the proportional hazards regression model with censored data Cain, K.C.;Lange, N.T. https://doi.org/10.2307/2531402
  10. Residuals and Influence in Regression Cook, D.R.;Weisberg, S.
  11. Journal of the Royal Statistical Society B v.30 A General Definition of Residuals(with Discussion) Cox, D.R.;Snell, E.J.
  12. Journal of the Royal Statistical Society B v.34 Regression models and life tables(with discussion) Cox, D.R.
  13. Biometrika v.62 Partial likelihood Cox, D.R. https://doi.org/10.1093/biomet/62.2.269
  14. Counting Processes and Survival Analysis Flemming, T.R.;Harrington, D.P
  15. Mathematical Centre Tracts 124 Censoring and Stochasic Integrals Gill, R.D.
  16. SAS Supplemental Library User's Guide(Version 5) The PHGLM procedure Harrel, F.
  17. Survival analysis: Techniques for censored and truncated data Klein, J.P.;Moeschberger, M.L.
  18. Generalized linear Models(2nd Ed.) McCullagh, P.;Nelder, J.A.
  19. Biometrika v.69 Partial residuals for the proportional hazards regression model Schoenfeld, D. https://doi.org/10.1093/biomet/69.1.239
  20. Data Analysis Products Division S-plus4 Guide to statistics MathSoft, Inc.
  21. Biometrika v.77 Martingale-based residuals for survival models Therneau, T. M.;Grambsch, P.M.;Flemming, T.R. https://doi.org/10.1093/biomet/77.1.147
  22. Modeling survival data: Extending the Cox Model Therneau, T. M.;Grambsch, P.M.