Putting background information about relative risks into conjugate prior distributions

Biometrics

Volumn 57, Issue 3, 2001, Pages 663-670

  Greenland, Sander ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Bayesian analysis; Data augmentation; Epidemiologic methods; Exponential regression; Interpretation; Log linear models; Logistic regression; Odds ratio; Poisson regression; Relative risk; Risk assessment; Risk regression

Indexed keywords

NORMAL DISTRIBUTION; POISSON DISTRIBUTION; REGRESSION ANALYSIS;

BAYESIAN ANALYSIS; DATA AUGMENTATION; EPIDEMIOLOGIC METHODS; EXPONENTIAL REGRESSION; INTERPRETATION; LOGISTICS REGRESSIONS; LOGLINEAR MODEL; ODD RATIOS; POISSON REGRESSION; RELATIVE RISKS; RISK REGRESSION; RISKS ASSESSMENTS;

RISK ASSESSMENT;

BAYESIAN ANALYSIS; EPIDEMIOLOGY; STATISTICAL ANALYSIS;

ARTICLE; BAYES THEOREM; DATA ANALYSIS; EPIDEMIOLOGY; REGRESSION ANALYSIS; RISK; RISK ASSESSMENT; STATISTICAL MODEL; STATISTICS;

EID: 0034875204     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.2001.00663.x     Document Type: Article

References (34)

1. A new perspective on generalized linear models
2. Bayesian binomial regression: Predicting survival at a trauma center
3. Approximate inference in generalized linear mixed models
4. Multiple imputation of industry and occupation codes in census public-use samples using Bayesian logistic regression
5. Bayes empirical Bayes
6. Exchangeability and data analysis
7. Large sample Bayesian inference on the parameters of the proportional hazards model
8. A semi-Bayes approach to the analysis of correlated associations, with an application to an occupational cancer-mortality study
9. Methods for epidemiologic analyses of multiple exposures: A review and comparative study of maximum-likelihood, preliminary-testing, and empirical-Bayes regression
10. Second-stage least squares versus penalized quasi-likelihood for fitting hierarchical models in epidemiologic analyses
11. Probability logic and probabilistic induction
12. When should epidemiologic regressions use random coefficients?
13. Data augmentation priors for Bayesian and semi-Bayes analyses of conditional-logistic and proportional hazards regression
14. Approximate Bayesian inference in conditionally independent hierarchical models
15. Advanced nonlinear regression in BMDP
16. Sensitivity analyses would help
17. The philosophy of statistics
18. General relativerisk regression models for epidemiologic studies
19. The effect of fetal monitoring on neonatal death rates
20. Prediction error and its estimation for subset-selected models
21. The calculation of posterior distributions by data augmentation
22. Multilevel modeling in epidemiology with GLIMMIX
23. Generalized linear mixed models: A pseudo-likelihood approach