DOI QR코드

DOI QR Code

Modified Product-Limit Estimator via Period Analysis

기간분석에 따른 수정된 누적한계 추정량

  • Kim, Jin-Heum (Department of Applied Statistics, University of Suwon) ;
  • Ahn, Yoon-Ok (Department of Preventive Medicine, Seoul National University College of Medicine)
  • 김진흠 (수원대학교 자연과학대학 통계정보학과) ;
  • 안윤옥 (서울대학교 의과대학 예방의학교실)
  • Published : 2006.11.30

Abstract

Long-term survival rates are the most commonly used outcome measures for patients with cancer. However, traditional long-term survival statistics, which are derived by cohort analysis or complete analysis, essentially reflect the survival expectations of patients diagnosed many years ago. They are often outdated at the time they become available. In this article, we propose a modified product-limit method to obtain up-to-date estimates of long-term survival rates via a period analysis. The proposed method is illustrated with cancer registry data collected from January 1993 to December 1997.

임상시험 연구나 역학 연구에서 환자들의 예후는 흔히 생존을 추정을 통해 수량화 되곤 한다. 하지만 코호트 분석이나 완전분석에 의한 생존율 추정량들은 수년 전에 진단된 환자에 크게 의존하기 때문에 실제 생존율보다 더 낮게 추정하곤 한다. 본 연구에서는 최근의 생존정보를 잘 반영하는 생존을 추정을 위해 기간분석 방법을 통한누적한계 추정량을 제안하였고, 그 방법을 1993년 1월-1997년 12월 사이에 조사된 서울시 암등록 자료(Ahn등, 2002)에 적용하여 결과를 고찰하였다.

Keywords

References

  1. Ahn Y. O., Shin M. H., Kim J. P. (2002). Korea, Seoul, In Cancer Incidence in Five Continents, Vol. VIII, IARC Scientific Publications No. 155 (Parkin, D.M., Whelan S.L., Ferlay, J., Teppo, L., and Thomas, D.B. eds), IARC, Lyon, 276-277
  2. Brenner, H. and Gefeller, O. (1996). An alternative approach to monitoring cancer patient survival, Cancer, 78, 2004-2010 https://doi.org/10.1002/(SICI)1097-0142(19961101)78:9<2004::AID-CNCR23>3.0.CO;2-#
  3. Brenner, H. and Gefeller, O. (1997). Deriving more up-to-date estimates of long-term patient survival, Journal of Clinical Epidemiology, 50, 211-216 https://doi.org/10.1016/S0895-4356(97)00280-1
  4. Brenner, H., Gefeller, O., Stegmaier, C., and Ziegler, H. (2001). More up-to-date monitoring of long-term survival rates by cancer registries: an empirical example, Methods of Information in Medicine, 40, 248-252 https://doi.org/10.1055/s-0038-1634161
  5. Brenner, H. (2002). Long-term survival rates of cancer patients achieved by the end of the 20th century: a period analysis, Lancet, 360, 1131-1135 https://doi.org/10.1016/S0140-6736(02)11199-8
  6. Brenner, H. and Hakulinen, T. (2002). Up-to-date long-term survival curves of patients with cancer by period analysis, Journal of Clinical Oncology, 20, 826-832 https://doi.org/10.1200/JCO.20.3.826
  7. Brenner, H., Soderman, B., and Hakulinen, T. (2002). Use of period analysis for providing more up-to-date estimates of long-term survival rates: empirical evaluation among 370,000 cancer patients in Finland, International Journal of Epidemiology, 31, 456-462 https://doi.org/10.1093/ije/31.2.456
  8. Breslow, N. E. and Crowley, J. (1974). A large sample study of the life table and product limit estimates under random censorship, Annals of Statistics, 2, 437-453 https://doi.org/10.1214/aos/1176342705
  9. Cutler, S. J. and Ederer, F. (1958). Maximum utilization of the life table method in analyzing survival, Journal of Chronic Diseases, 8, 699-712 https://doi.org/10.1016/0021-9681(58)90126-7
  10. Greenwood, M. (1926). The natural duration of cancer, In Reports on Public Health and Medical Subjects, 33, Her Majesty's Stationery Office, London, 1-26
  11. Kaplan, E. L. and Meier, P. (1958). Nonparametric estimation from incomplete observations, Journal of the American Statistical Association, 58, 457-481
  12. Thompson, W. A., Jr. (1977). On the treatment of grouped observations in life studies, Biometrics, 33, 463-470 https://doi.org/10.2307/2529360