Large hierarchical bayesian analysis of multivariate survival data

Biometrics

Volumn 53, Issue 1, 1997, Pages 230-242

  Gustafson, Paul ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Frailty model; Hierarchical model; Hybrid Monte Carlo; Markov chain Monte Carlo; Multicenter clinical trial; Survival analysis

Indexed keywords

HIERARCHICAL SYSTEMS; MARKOV PROCESSES; MEDICAL APPLICATIONS; MONTE CARLO METHODS; MULTIVARIANT ANALYSIS;

FRAILTY MODELS; HIERARCHICAL BAYESIAN ANALYSIS; HIERARCHICAL MODEL; HYBRID MONTE CARLO; MARKOV CHAIN MONTE CARLO; MARKOV CHAIN MONTE CARLO METHOD; MARKOV CHAIN MONTE-CARLO; MULTI-CENTER CLINICAL TRIALS; SURVIVAL ANALYSIS; SURVIVAL DATA;

CHAINS;

ARTICLE; BAYES THEOREM; DATA ANALYSIS; MULTIVARIATE ANALYSIS; STATISTICAL MODEL; SURVIVAL;

EID: 0030980644     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.2307/2533110     Document Type: Article

References (28)

1. Alder, B. J. and Wainwright, T. E. (1959). Studies in molecular dynamics. I. General method. Journal of Chemical Physics 31, 459-466.
2. Andersen, H. C. (1980). Molecular dynamics simulations at constant pressure and/or temperature. Journal of Chemical Physics 72, 2384-2393.
3. Besag, J., Green, P., Higdon, D., and Mengersen, K. (1995). Bayesian computation and stochastic systems (with discussion). Statistical Science 10, 3-36.
4. Clayton, D. G. (1991). A Monte Carlo method for Bayesian inference in frailty models. Biometrics 48, 61-72.
5. Cowles, M. K. and Carlin, B. P. (1996). Markov chain Monte Carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association 91, 883-904.
6. Duane, S., Kennedy, A. D., Pendleton, B. J., and Roweth, D. (1987). Hybrid Monte Carlo. Physical Letters B 195, 216-222.
7. Gray, R. J. (1994). A Bayesian analysis of institutional effects in a multicenter cancer clinical trial. Biometrics 50, 244-253.
8. Gustafson, P. (1995). A Bayesian analysis of bivariate survival data from a multicentre cancer clinical trial. Statistics in Medicine 14, 2523-2535.
9. Gustafson, P. (1996). The effect of mixing distribution misspecification in conjugate mixture models. Canadian Journal of Statistics 24, 307-318.
10. Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 97-109.
11. Kalbfleisch, J. D. (1978). Non-parametric Bayesian analysis of survival time data. Journal of the Royal Statistical Society, Series B 40, 214-221.
12. Kalbfleisch, J. D. and Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. Wiley. New York.
13. Kass, R. E. and Steffey, D. (1989). Approximate Bayesian inference in conditionally independent hierarchical models (parametric empirical Bayes models). Journal of the American Statistical Association 84, 717-726.
14. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics 21, 1087-1092.
15. Neal, R. M. (1993a). Bayesian learning via stochastic dynamics. In Advances in Neural Information Processing Systems 5, C. L. Giles, S. J. Hanson, and J. D. Cowan (eds), 475-482. San Mateo, California: Morgan Kaufmann.
16. Neal, R. M. (1993b). Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto, Toronto, Canada.
17. Neal, R. M. (1994a). An improved acceptance procedure for the hybrid Monte Carlo algorithm. Journal of Computational Physics 111, 194-204.
18. Neal, R. M. (1994b). Bayesian learning for neural networks. Ph.D.thesis, Department of Computer Science, University of Toronto, Toronto, Canada.
19. Neuhaus, J. M., Hauck, W. W., and Kalbfleisch, J. D. (1992). The effects of mixture distribution misspecification when fitting mixed-effects logistic models. Biometrika 79, 755-762.
20. Neuhaus, J. M., Kalbfleisch, J. D., and Hauck, W. W. (1994). Conditions for consistent estimation in mixed-effects models for binary matched pairs data. Canadian Journal of Statistics 22, 139-148.
21. Pickles, A. and Crouchley, R. (1995). A comparison of frailty models for multivariate survival data. Statistics in Medicine 14, 1447-1461.
22. Poon, M. A., O'Connell, M. J., Moertel, C. G., Wieand, H. S., Cullinan, S. A., Everson, L. K., Krook, J. E., Mailliard, J. A., Laurie, J. A., Tschetter, L. K., and Wiesenfeld, M. (1989). Biochemical modulation of fluorouracil: Evidence of significant improvement of survival and quality of life in patients with advanced colorectal carcinoma. Journal of Clinical Oncology 7, 1407-1418.
23. Sinha, D. (1993). Semiparametric Bayesian analysis of multiple event time data. Journal of the American Statistical Association 88, 979-983.
24. Smith, A. F. M. and Gelfand, A. E. (1992). Bayesian statistics without tears: A sampling-resampling perspective. American Statistician 46, 84-88.
25. Smith, A. F. M. and Roberts, G. O. (1993). Bayesian computation via the, Gibbs sampler and related Markov chain Monte Carlo methods (with discussion). Journal of the Royal Statistical Society, Series B 55, 3-23.
26. Stangl, D. (1995). Prediction and decision making using Bayesian hierarchical models. Statistics in Medicine 14, 2173-2190.
27. Stangl, D. and Greenhouse, J. B. (1995). Assessing placebo response using Bayesian hierarchical survival models. Discussion Paper 95-01, Institute of Statistics and Decision Sciences, Duke University, Durham, North Carolina.
28. Tierney, L. (1994). Markov chains for exploring posterior distributions (with discussion). Annals of Statistics 22, 1701-1762.