DOI QR코드

DOI QR Code

Simulation Study for Statistical Methods in Comparing Cure Rates between Two Groups

모의실험을 통한 두 처리군간 치료율 비교방법 연구

  • Published : 2004.07.01

Abstract

In some clinical trials, one may see that a significant fraction of patients are cured and their original disease does not recur even after termination of treatment and pro-longed follow-up. This situation occurs frequently in pediatric cancer trials where there are excellent therapeutic results. In such cases, interest concentrated on the difference of cure rates rather than other types of differences in failure distributions. Various authors have investigated the parametric and nonparametric methods for testing the difference of cure rates. In this study, we compare by simulation the power and size of a parametric test and five nonparametric tests in a various range of the alternatives, censoring rates and cure rates. Our objectives are to determine if any test was preferable on the basis of size and power in various situation, and to investigate the effect of the model misspecification.

임상시험중에는 소아암연구에서와 같이 환자 중의 상당수에서 사망 또는 재발이 오랜 기간 일어나지 않고 완치된 것으로 보이는 경우가 있다. 이 경우 연구자는 생존함수의 전반적인 비교보다는 치료율의 비교에 더 관심이 있을 것이다. 본고에서는 치료율의 비교를 위한 여러 모수적, 비모수적 방법들을 소개하고, 생존분포, 치료율, 중도절단을 등을 다양하게 설정한 모의실험을 통하여 각 방법들의 검정력과 유의수준을 비교하였다.

Keywords

References

  1. Communication in statistics A-Theory and Methods v.14 A family of multiplicative survival models incorporating a long-term survivorship parameter C as a function of covariates Arbutiski, T. https://doi.org/10.1080/03610928508829000
  2. The Analysis Binary Data Cox, D. R.
  3. Biometrics v.38 The use of mixture models for the analysis of survival data with long-term survivors Farewell, V. T. https://doi.org/10.2307/2529885
  4. Statistics in Medicine v.3 Survivorship analysis when cure is a possibility: a Monte Carlo study Goldman, A. https://doi.org/10.1002/sim.4780030208
  5. Biometrics v.45 A linear rank test for use when the main interest is in difference in cure rates Gray, R. J.;Tsiatis, A. A. https://doi.org/10.2307/2531691
  6. Journal of Multivariate Analysis v.49 Exponential mixture models with long-term survivors and covariates Guitany, M. E.;Maller, R. A.;Zhou, S. https://doi.org/10.1006/jmva.1994.1023
  7. Biometrie-Praximetrie v.21 On estimaing the proportion of cured patients in clinical studies Jones, D. R.;Powels, R. L.;Machin, D.;Sylvester, R. J.
  8. The Statistical Analysis of Failure Time Data Kalbfleish, J. D.;Prentice, R. L.
  9. Biometrika v.79 A mixture model combining logistic regression with proportional hazards regression Kuk, Y. C.;Chen, C. H. https://doi.org/10.1093/biomet/79.3.531
  10. Biometrics v.48 Nonparametric estimation and testing in a cure model Laska, E. M.;Meisner, M. J. https://doi.org/10.2307/2532714
  11. Biometrics v.51 Group sequential methods for comparison of cure rates in clinical trials Lee, J. W.;Sather, H. L. https://doi.org/10.2307/2532962
  12. Statistics in Medicine v.17 A generalized F mixture model for cure rate estimation Peng, Y.;Dear, K. B. G.;Denham, J. W. https://doi.org/10.1002/(SICI)1097-0258(19980430)17:8<813::AID-SIM775>3.0.CO;2-#
  13. Biometrics v.56 A nonparametric mixture model for cure rate estimation Peng, Y.;Dear, K. B. G. https://doi.org/10.1111/j.0006-341X.2000.00237.x
  14. Biometrics v.48 A comparison of tests of the difference in the proportion of patients who are cured Sposto, R.;Sather, H. N.;Baker, S. A. https://doi.org/10.2307/2532741
  15. Biometrics v.56 Estimation in a Cox proprtional hazards cure model Sy, J. P.;Taylor, J. M. G. https://doi.org/10.1111/j.0006-341X.2000.00227.x
  16. Biometrika v.64 On distribution free tests for equality of survival distributions Tarone, R. E.;Ware, J. https://doi.org/10.1093/biomet/64.1.156
  17. Journal of the American Statistical Association v.87 Accelerated failure-time regression models with a regression model of surviving fraction: an application to the analysis of permenent employment in Japan Yamaguchi, K. https://doi.org/10.2307/2290258