Estimation in a Cox proportional hazards cure model

Biometrics

Volumn 56, Issue 1, 2000, Pages 227-236

  Sy, Judy P ^a   Taylor, Jeremy M G ^b  

^a GENENTECH INC   (United States)

^b UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Cure model; EM algorithm; Product limit estimate; Profile likelihood

Indexed keywords

HAZARDS; INVERSE PROBLEMS; MAXIMUM LIKELIHOOD ESTIMATION; PATIENT MONITORING; POPULATION STATISTICS; RADIOTHERAPY;

CURE MODELS; EM ALGORITHMS; FAILURE TIME DATA; FOLLOW UP; PRODUCT-LIMIT; PRODUCT-LIMIT ESTIMATE; PROFILE LIKELIHOOD; PROPORTIONAL HAZARDS CURE MODEL; SURVIVAL ANALYSIS; UP PERIOD;

CURING;

ALGORITHM; ARTICLE; CANCER RADIOTHERAPY; DISEASE PREDISPOSITION; DISEASE SIMULATION; HAZARD; PROBABILITY; STATISTICAL MODEL; SURVIVAL; TONSIL CANCER;

EID: 0034021942     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.2000.00227.x     Document Type: Article

References (16)

1. Andersen, P. K. and Gill, R. D. (1982). Cox's regression model for counting processes: A large sample study. Annals of Statistics 10, 1100-1120.
2. Bailey, K. R. (1984). Asymptotic equivalence between the Cox estimator and the general ML estimators of regression and survival parameters in the Cox model. Annals of Statistics 12, 730-736.
3. Breslow, N. E. (1972). Contribution to the discussion of D. R. Cox (1972). Journal of the Royal Statistical Society, Series B 34, 216-217.
4. Farewell, V. T. (1982). The use of mixture models for the analysis of survival data with long-term survivors. Biometrics 38, 1041-1046.
5. Farewell, V. T. (1986). Mixture models in survival analysis: Are they worth the risk? Canadian Journal of Statistics 14, 257-262.
6. Kalbfleisch, J. D. and Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. New York: Wiley.
7. Klein, J. P. (1992). Semiparametric estimation of random effects using the Cox model based on the EM algorithm. Biometrics 48, 795-806.
8. Kuk, A. Y. C. and Chen, C. H. (1992). A mixture model combining logistic regression with proportional hazards regression. Biometrika 79, 531-541.
9. Louis, T. A. (1982). Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society, Series B 44, 226-233.
10. Maller, R. A. and Zhou, S. (1996). Survival Analysis with Long Term Survivors. New York: Wiley.
11. 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.
12. Prentice, R. L. and Gloeckler, L. A. (1978). Regression analysis of grouped survival data with application to breast cancer data. Biometrics 34, 57-67.
13. Sy, J. P. and Taylor, J. M. G. (1999). Standard errors for the Cox proportional hazards curve model. Mathematical and Computer Modelling, in press.
14. Taylor, J. M. G. (1995). Semi-parametric estimation in failure time mixture models. Biometrics 51, 899-907.
15. Tsiatis, A. A. (1981). A large sample study of Cox's regression model. Annals of Statistics 9, 93-108.
16. Withers, H. R., Peters, L. J., Taylor, J. M. G., et al. (1995). Local control of carcinoma of the tonsil by radiation therapy: An analysis of patterns of fractionation in nine institutions. International Journal of Radiation Oncology, Biology, Physics 33, 549-562.