Gibbs sampling for long-term survival data with competing risks

Biometrics

Volumn 54, Issue 1, 1998, Pages 350-366

  Chao, Edward C ^a  

^a Parametric Technology Corporation   (United States)

Author keywords

Bone marrow transplant; Curability; Data augmentation algorithms; Mixture models

Indexed keywords

BONE; DISEASES; PATIENT TREATMENT;

BONE MARROW TRANSPLANT; COMPETING RISKS; CURABILITY; DATA AUGMENTATION ALGORITHMS; GIBBS SAMPLING; GRAFT-VERSUS-HOST DISEASE; MEASUREMENTS OF; MIXTURE MODELING; SURVIVAL DATA; SURVIVAL PROBABILITIES;

CURING;

ARTICLE; BONE MARROW TRANSPLANTATION; GRAFT VERSUS HOST REACTION; MATHEMATICAL MODEL; PREDICTION; PROBABILITY; RISK; SAMPLING; SURVIVAL TIME;

EID: 0031920603     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.2307/2534022     Document Type: Article

References (21)

1. Berkson, J. and Gage, R. P. (1952). Survival curve for cancer patients following treatment. Journal of the American Statistical Association 47, 501-515.
2. Boag, J. W. (1949). Maximum likelihood estimation of the proportion of patients cured by cancer treatment. Journal of the Royal Statistical Society 11, 15-53.
3. Chao, E. C. (1994). A comparison of models including possibility of cure for survival data. Ph.D. dissertation, University of Michigan, Ann Arbor.
4. Chen, W. C., Hill, B. M., Greenhouse, J. B., and Fayos, J. V. (1985). Bayesian analysis of survival curves for cancer patients following treatment. Bayesian Statistics 2, 299-328.
5. Clift, R. A., Appelbaum, F. R., and Thomas, E. D. (1993). Treatment of chronic myeloid leukemia by marrow transplantation. Blood 82, 1954-1956.
6. Farewell, V. T. (1982). The use of mixture models for the analysis of survival data with long-term survivors. Biometrics 38, 1041-1046.
7. Farewell, V. T. (1986). Mixture models in survival analysis: Are they worth the risk? Canadian Journal of Statistics 14, 257-262.
8. Gelfand, A. E. and Smith, A. F. M. (1990). Sampling based approaches to calculating marginal densities. Journal of the American Statistical Association 85, 398-409.
9. Gelman, A. and Rubin, D. (1992). Inference from iterative simulation using multiple sequences. Statistical Science 7, 4570-511.
10. Ghitany, M. E. and Maller, R. A. (1992). Asymptotic results for exponential mixture models with long-term survivors. Statistics 23, 321-336.
11. 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.
12. Gordon, N. H. (1990). Application of the theory of finite mixtures for the estimation of 'cure' rates of treated cancer patients. Statistics in Medicine 9, 397-407.
13. Greenhouse, J. and Wolfe, R. A. (1984). A competing risks derivation of a mixture model for the analysis of survival data. Communication in Statistics A 13, 3133-3154.
14. Kuk, A. Y. C. and Chen, C.-H. (1992). A mixture model combining logistic regression with proportional hazards regression. Biometrika 79, 531-541.
15. Larson, M. and Dinse, G. E. (1985). A mixture model for the regression analysis of competing risks data. Applied Statistics 34, 201-211.
16. Meeker, W. Q. (1987). Limited failure population life tests: Application to integrated circuit reliability. Technometrics 29, 51-65.
17. Pepe, M. S. and Mori, M. (1993). Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? Statistics in Medicine 12, 737-748.
18. Slattery, J. T., Clift, R. A., Buckner, C. D., et al. (1997). Marrow transplantation for chronic myeloid leukemia: The influence of plasma busulfan levels on the outcome of transplantation. Blood 89, 3055-3060.
19. Sullivan, K. M., Weiden, P. L., Storb, R., et al. (1989). Influence of acute and chronic graft-versus-host disease on relapse and survival after bone marrow transplantation from HLA-identical siblings as treatment of acute and chronic leukemia. Blood 73, 1720-1728.
20. Tanner, M. A. and Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association 82, 528-549.
21. Taylor, J. M. G. (1995). Semi-parametric estimation in failure time mixture models. Biometrics 51, 899-907.