Generalized Conjugate Priors for Bayesian Analysis of Risk and Survival Regressions

DOI10.1111/1541-0420.00011zbMath1210.62026OpenAlexW2029722537WikidataQ43840274 ScholiaQ43840274MaRDI QIDQ3079080

Sander Greenland

Publication date: 1 March 2011

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1541-0420.00011

zbMATH Keywords

risk analysislogistic regressionrelative riskCox modelconjugate priorPoisson regressioncase-control studiesBayesian inferenceodds ratiorisk assessmentrisk ratiosurvival regressionepidemiologic methodsproportional-hazards regression

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Survival analysis and censored data (62N99)

Related Items

Bayesian solution to the monotone likelihood in the standard mixture cure model, Conjugate priors and bias reduction for logistic regression models, Relaxation penalties and priors for plausible modeling of nonidentified bias sources, Connecting logistic probability models with basic dynamic processes, Multiple-Bias Modelling for Analysis of Observational Data, The Performance of Random Coefficient Regression in Accounting for Residual Confounding

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bayesianly justifiable and relevant frequency calculations for the applied statistician
• Putting Background Information About Relative Risks into Conjugate Prior Distributions
• Logistic disease incidence models and case-control studies
• Some results on the estimation of logistic models based on retrospective data
• Sampling and Bayes' Inference in Scientific Modelling and Robustness
• A New Perspective on Priors for Generalized Linear Models
• P Values for Composite Null Models
• Asymptotic Distribution of P Values in Composite Null Models
• When Should Epidemiologic Regressions Use Random Coefficients?
• Hypothesis testing in case-control studies
• Separate sample logistic discrimination