Browse > Article
http://dx.doi.org/10.5351/CKSS.2008.15.5.727

Testing Relationship between Treatment and Survival Time with an Intermediate Event  

Lee, Sung-Im (Department of Statistics, DanKook University)
Publication Information
Communications for Statistical Applications and Methods / v.15, no.5, 2008 , pp. 727-735 More about this Journal
Abstract
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.
Keywords
Survival; intermediate event; two-stage model; likelihood ratio test;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 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   DOI   ScienceOn
2 Finkelstein, D. M. and Scheonfeld, D. A. (1994). Analysing survival in the presence of an auxiliary variable, Statistics in Medicine, 13, 1747-1754   DOI   ScienceOn
3 Lee, J. W. (1994). An overview of group sequential procedures, The Korean Journal of Applied Statistics, 7, 35-51
4 Lefkopoulou, M. and Zelen, M. (1995). Intermediate clinical events, surrogate markers and survival, Lifetime Data Analysis, 1, 73-85   DOI
5 Nam, C. and Zelen, M. (2001). Comparing the survival of two groups with an intermediate clinical event, Lifetime Data Analysis, 7, 5-19   DOI   ScienceOn
6 Anderson, J. R., Cain, K. C. and Gelber, R. D. (1983). Analysis of survival by tumor response, Journal of Clinical Oncology, 1, 710-719   DOI
7 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   DOI   ScienceOn
8 Lagakos, S. W. (1976). A stochastic model for censored-survival data in the presence of an auxiliary variable, Biometrics, 32, 551-559   DOI   ScienceOn
9 Crowley, J. and Hu, M. (1977). Covariance analysis of heart transplant survival data, Journal of the American Statistical Association, 72, 27-36   DOI
10 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   과학기술학회마을
11 Lee, J. W. and Sather, H. N. (1995). Group sequential methods for comparison of cure rates in clinical trials, Biometrics, 51, 756-763   DOI   ScienceOn