Bayesian information criterion for censored survival models

Biometrics

Volumn 56, Issue 1, 2000, Pages 256-262

  Volinsky, Chris T ^a   Raftery, Adrian E ^b  

^a AT AND T LABS RESEARCH   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Bayes factor; Cox proportional hazards model; Exponential distribution; Partial likelihood; Variable selection

Indexed keywords

BARIUM COMPOUNDS; HAZARDS; HEALTH RISKS; RISK ASSESSMENT;

BAYES FACTOR; BAYESIAN INFORMATION CRITERION; COX PROPORTIONAL HAZARD MODEL; EXPONENTIAL DISTRIBUTIONS; PARAMETER SPACES; PARTIAL LIKELIHOODS; PENALTY TERM; SURVIVAL DATA; SURVIVAL MODEL; VARIABLES SELECTIONS;

REGRESSION ANALYSIS;

ARTICLE; BAYES THEOREM; CARDIOVASCULAR DISEASE; STATISTICAL ANALYSIS; STATISTICAL MODEL; STROKE; SURVIVAL;

EID: 0034059779     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.2000.00256.x     Document Type: Article

References (20)

1. Cox, D. R. (1972). Regression models and life tables with discussion. Journal of the Royal Statistical Society, Series B 34, 187-220.
2. Dickson, E. R., Fleming, R. R., and Weisner, R. H. (1985). Trial of penicillamine in advanced primary biliary cirrhosis. New England Journal of Medicine 312, 1011-1015.
3. Fleming, T. R. and Harrington, D. H. (1991). Counting Processes and Survival Analysis. New York: Wiley.
4. Fried, L. P., Borhani, N. O., Enright, P., et al. (1991). The Cardiovascular Health Study: Design and rationale. Annals of Epidemiology 1, 263-276.
5. Good, I. J. (1952). Rational decisions. Journal of the Royal Statistical Society, Series B 14, 107-114.
6. Kalbfleisch, J. D. and Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. New York: Wiley.
7. Kass, R. E. and Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association 90, 773-795.
8. Kass, R. E. and Wasserman, L. (1995). A reference Bayesian test for nested hypotheses with large samples. Journal of the American Statistical Association 90, 928-934.
9. Madigan, D. and Raftery, A. E. (1994). Model selection and accounting for model uncertainty in graphical models using Occam's Window. Journal of the American Statistical Association 89, 1535-1546.
10. Madigan, D., Gavrin, J., and Raftery, A. E. (1995). Eliciting prior information to enhance the predictive performance of Bayesian graphical models. Communications m Statistics - Theory and Methods 24, 2271-2292.
11. Manolio, T. A., Kronmal, R. A., Burke, G. L., et al. (1996). Short-term predictors of incident stroke in older adults. Stroke 27, 1479-1486.
12. Paganini-Hill, A., Chao, A., Ross, R., and Henderson, B. (1989). Aspirin use and chronic diseases: A cohort study of the elderly. British Medical Journal 299, 1247-1250.
13. Prentice, R. L. (1973). Exponential survivals with censoring and explanatory variables. Biometrika 60, 279-288.
14. Raftery, A. E. (1995). Bayesian model selection in social research (with discussion). In Sociological Methodology, P. V. Marsden (ed), 111-195. Cambridge, Massachusetts: Blackwell.
15. Raftery, A. E., Madigan, D., and Hoeting, J. A. (1997). Bayesian model averaging for linear regression models. Journal of the American Statistical Association 92, 179-191.
16. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics 6, 461-464.
17. Steering Committee of the Physicians' Health Study. (1989). Final report on the aspirin component of the ongoing physicians' health study. New England Journal of Medicine 321, 129-135.
18. Stroke Prevention in Atrial Fibrillation Study Group Investigators. (1990). Preliminary report of the stroke prevention in atrial fibrillation. New England Journal of Medicine 322, 863-868.
19. Volinsky, C. T. and Raftery, A. E. (1999). Bayesian information criterion for censored survival models. Technical Report 349, Department of Statistics, University of Washington, Seattle. (http://www.stat.Washington. edu/tech.reports/tr349.ps).
20. Volinsky, C. T., Madigan, D., Raftery, A. E., and Kronmal, R. A. (1997). Bayesian model averaging in proportional hazard models: Assessing the risk of a stroke. Applied Statistics 46, 443-448.