Bayesian analysis and model selection for interval-censored survival data

Biometrics

Volumn 55, Issue 2, 1999, Pages 585-590

  Sinha, Debajyoti ^a   Chen, Ming Hui ^b   Ghosh, Sujit K ^c  

^a Univ of New Hampshire   (United States)

^b Worcester Polytechnic Institute   (United States)

^c NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

CPO; Gibbs sampler; Prior process

Indexed keywords

SAMPLING;

ANALYSIS AND MODELS; BAYESIAN ANALYSIS; BAYESIAN MODELLING; CENSORED DATA; CPO; GIBBS SAMPLERS; INTERVAL-CENSORED SURVIVAL DATA; MODEL SELECTION; PRIOR PROCESS; SURVIVAL ANALYSIS;

STATISTICAL TESTS;

ARTICLE; BAYES THEOREM; BIOMETRY; BREAST CANCER; CANCER SURVIVAL; HUMAN; MATHEMATICAL COMPUTING; METHODOLOGY; STATISTICAL ANALYSIS; STATISTICAL MODEL; SURVIVAL RATE;

EID: 0033006795     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.1999.00585.x     Document Type: Article

References (14)

1. Cowles, M. K. and Carlin, B. P. (1996). Markov chain Monte Carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association 90, 883-904.
2. Cox, D. R. (1972). Regression models and life tables. Journal of the Royal Statistical Society, Series B 34, 187-220.
3. Finkelstein, D. M. (1986). A proportional hazards model for interval-censored failure time data. Biometrics 42, 845-854.
4. Finkelstein, D. M. and Wolf, R. A. (1985). A semiparametric model for regression analysis of interval-censored failure time data. Biometrics 41, 933-945.
5. Frydman, H. (1995). Nonparametric estimation of a Markov 'illness-death' process from interval-censored observations, with application to diabetes survival data. Biometrika 82, 773-790.
6. Gelfand, A. E. and Ghosh, S. K. (1998). Model choice: A minimum posterior predictive loss approach. Biometrika 85, 1-12.
7. Gelfand, A. E., Dey, D. K., and Chang, H. (1992). Model determination using predictive distributions with implementation via sampling-based methods. In Bayesian Statistics, Volume 4. J. M. Bernado, J. O. Berger, A. P. Dawid, and A. F. M. Smith (eds), 147-167. Oxford: Oxford University Press.
8. Gilks, W. R. and Wild, P. (1992). Adaptive rejection sampling for Gibbs sampling. Applied Statistics 41, 337-348.
9. Kalbfleisch, J. D. (1978). Nonparametric Bayesian analysis of survival time data. Journal of the Royal Statistical Society, Series B 40, 214-221.
10. Laud, P. W. and Ibrahim, J. G. (1995). Predictive model selection. Journal of the Royal Statistical Society, Series B 57, 247-262.
11. Sargent, D. J. (1996). A flexible approach to time-varying coefficients in the Cox regression setting. Technical Report, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis.
12. Satten, G. A. (1996). Rank based inference in the proportional hazards model for interval censored data. Biometrika 83, 355-370.
13. Sinha, D. and Dey, D. K. (1997). Semiparametric Bayesian methods for survival data. Journal of the American Statistical Association 92, 1195-1212.
14. Tanner, M. A. (1996). Tools for Statistical Inference, 3rd edition. New York: Springer Verlag.