An empirical comparison of parametric and semiparametric cure models

Biometrical Journal

Volumn 44, Issue 8, 2002, Pages 1002-1014

  Peng, Yingwei ^a   Carriere, K C ^b  

^a MEMORIAL UNIVERSITY OF NEWFOUNDLAND   (Canada)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Censored data; Cure proportion; EM algorithm; Extended generalized gamma distribution; Generalized F distribution; Kaplan Meier survival estimator; Mixture model; Proportional hazards model; Weibull distribution

Indexed keywords

CLINICAL RESEARCH; CURING; HAZARDS; PARAMETER ESTIMATION;

CENSORED DATA; CURE PROPORTION; EM ALGORITHMS; EXTENDED GENERALIZED GAMMA DISTRIBUTION; F DISTRIBUTION; GENERALIZED F DISTRIBUTION; GENERALIZED GAMMA DISTRIBUTION; KAPLAN-MEIER; KAPLAN-MEIER SURVIVAL ESTIMATOR; MIXTURE MODELING; PROPORTIONAL HAZARD MODELS;

WEIBULL DISTRIBUTION;

EID: 0036929419     PISSN: 03233847     EISSN: None     Source Type: Journal    
DOI: 10.1002/bimj.200290000     Document Type: Article

References (22)

1. Boag, J.W., 1949: Maximum likelihood estimates of the proportion of patients cured by cancer therapy. Journal of the Royal Statistical Society, 11, 15-53.
2. Cantor, A.B. and Shuster, J.J., 1992: Parametric versus non-parametric methods for estimating cure rates based on censored survival data. Statistics in Medicine, 11, 931-937.
3. Farewell, V.T., 1982: The use of mixture models for the analysis of survival data with long-term survivors. Biometrics, 38, 1041-46.
4. Farewell, V.T., 1986: Mixture models in survival analysis: Are they worth the risk? The Canadian Journal of Statistics, 14 (3), 257-262.
5. Gamel, J.W., McLean, I.W., and Rosenberg, S.H., 1990: Proportion cured and mean log survival time as functions of tumor size. Statistics in Medicine, 9, 999-1006.
6. Gamel, J.W. and Vogel, R.L., 1997: Comparison of parametric and non-parametric survival methods using simulated clinical data. Statistics in Medicine, 16, 1629-1643.
7. Ghitany, M.E., Maller, R.A., and Zhou, S., 1994: Exponential mixture models with long-term survivors and covariates. Journal of Multivariate Analysis, 49, 218-241.
8. Ghitany, M.E., Maller, R.A., and Zhou, S., 1995: Estimating the proportion of immunes in censored samples: A simulation study. Statistics in Medicine, 14, 39-49.
9. Goldman, A.L, 1984: Survivorship analysis when cure is a possibility: A Monte Carlo study. Statistics in Medicine, 3, 153-163.
10. Gordon, N.H., 1990a: Application of the theory of finite mixtures for the estimation of 'cure' rates of treated cancer patients. Statistics in Medicine, 9, 397-407.
11. Gordon, N.H., 1990b: Maximum likelihood estimation for mixtures of two gompertz distributions when censoring occurs. Communications in Statistics: Simulation and Computation, 19 (2), 733-47.
12. Jones, D.R., Powles, R.L., Machin, D., and Sylvester, R.J., 1981: On estimating the proportion of cured patients in clinical studies. Biometrie-Praximetrie, 21, 1-11.
13. Kalbfleisch, J.D. and Prentice, R.L., 1980: The Statistical Analysis of Failure Time Data. John Wiley and Sons, New York.
14. Klein, J.P. and Moeschberger, M.L., 1997: Survival Analysis, Techniques for Censored and Truncated Data. Spinger-Verlag, New York.
15. Kuk, A.Y.C. and Chen, C., 1992: A mixture model combining logistic regression with proportional hazards regression. Biometrika, 79, 531-41.
16. Maller, R.A. and Zhou, S., 1992: Estimating the proportion of immunes in a censored sample. Biometrika, 79 (4), 731-739.
17. MathSoft Inc. S-PLUS 2000 Programmer's Guide. Seattle, WA, 1999.
18. Peng, Y. and Dear, K.B.G., 2000: A nonparametric mixture model for cure rate estimation. Biometrics, 56, 237-243.
19. Peng, Y., Dear, K.B.G., and Denham, J.W., 1998: A generalized F mixture model for cure rate estimation. Statistics in Medicine, 17, 813-830.
20. Sy, J.P. and Taylor, J.M.G., 2000: Estimation in a Cox proportional hazards cure model. Biometrics, 56, 227-236.
21. Taylor, J.M.G., 1995: Semi-parametric estimation in failure time mixture models. Biometrics, 51, 899-907.
22. Yamaguchi, K., 1992: Accelerated failure-time regression models with a regression model of surviving fraction: An application to the analysis of 'permanent employment' in Japan. Journal of the American Statistical Association, 87 (418), 284-292.