DOI QR코드

DOI QR Code

Testing Relationship between Treatment and Survival Time with an Intermediate Event

Lee, Sung-Im

  • 발행 : 2008.09.30

초록

Consider a clinical trial in which the main end-point is survival. Suppose after the start of the study an intermediate event occurs which may be influenced by a covariate(or treatment). In many clinical studies the occurrence of an intermediate event may change the survival distribution. This investigation develops two-stage model which, in the first stage, models the effect of covariate on the intermediate event and models the relationship between survival time and covariate as well as the intermediate event. In this paper, the two-stage model is presented in order to model intermediate event and a test based on this model is also provided. A numerical simulations are carried out to evaluate its overall significance level.

키워드

Survival;intermediate event;two-stage model;likelihood ratio test

참고문헌

  1. Fleming, T. R., Prentice, R. L., Pepe, M. S. and Glidden, D. (1994). Surrogate and auxiliary endpoints in clinical trials, with potential applications in cancer and AIDS research, Statistics in Medicine, 13, 955-968 https://doi.org/10.1002/sim.4780130906
  2. Lagakos, S. W. (1976). A stochastic model for censored-survival data in the presence of an auxiliary variable, Biometrics, 32, 551-559 https://doi.org/10.2307/2529744
  3. Crowley, J. and Hu, M. (1977). Covariance analysis of heart transplant survival data, Journal of the American Statistical Association, 72, 27-36 https://doi.org/10.2307/2286902
  4. Anderson, J. R., Cain, K. C. and Gelber, R. D. (1983). Analysis of survival by tumor response, Journal of Clinical Oncology, 1, 710-719 https://doi.org/10.1200/JCO.1983.1.11.710
  5. Lee, J. W. (1998). A study on the group sequential methods for comparing survival distributions in clinical trials, The Korean Communications in Statistics, 5, 459-475
  6. Lee, J. W. and Sather, H. N. (1995). Group sequential methods for comparison of cure rates in clinical trials, Biometrics, 51, 756-763 https://doi.org/10.2307/2532962
  7. Nam, C. and Zelen, M. (2001). Comparing the survival of two groups with an intermediate clinical event, Lifetime Data Analysis, 7, 5-19 https://doi.org/10.1023/A:1009609925212
  8. Finkelstein, D. M. and Scheonfeld, D. A. (1994). Analysing survival in the presence of an auxiliary variable, Statistics in Medicine, 13, 1747-1754 https://doi.org/10.1002/sim.4780131706
  9. Lee, J. W. (1994). An overview of group sequential procedures, The Korean Journal of Applied Statistics, 7, 35-51
  10. Lefkopoulou, M. and Zelen, M. (1995). Intermediate clinical events, surrogate markers and survival, Lifetime Data Analysis, 1, 73-85 https://doi.org/10.1007/BF00985259
  11. Nan, B., Lin, X., Lisabeth, L. D. and Harlow, S. D. (2005). A varying-coefficient Cox model for the effect of age at a marker event on age at menopause, Biometrics, 61, 576-583 https://doi.org/10.1111/j.1541-0420.2005.030905.x

피인용 문헌

  1. 1. Testing the effect of treatment on survival time with an immediate intermediate event vol.46, pp.8, 2017, doi:10.5351/CKSS.2008.15.5.727