title: Generalized conjugate priors for Bayesian analysis of risk and survival regressions.
abstract: Conjugate priors for Bayesian analyses of relative risks can be quite restrictive, because their shape depends on their location. By introducing a separate location parameter, however, these priors generalize to allow modeling of a broad range of prior opinions, while still preserving the computational simplicity of conjugate analyses. The present article illustrates the resulting generalized conjugate analyses using examples from case-control studies of the association of residential wire codes and magnetic fields with childhood leukemia.
authors.affiliation: Department of Epidemiology, UCLA School of Public Health, 22333 Swenson Drive, Topanga, California 90290, USA. lesdomes@ucla.edu
authors.forename: Sander
authors.initials: S
authors.lastname: Greenland
mesh_terms: D001499:Bayes Theorem; D016022:Case-Control Studies; D002648:Child; D003627:Data Interpretation, Statistical; D004574:Electromagnetic Fields / Q000009:adverse effects; D006801:Humans; D007938:Leukemia / Q000453:epidemiology / Q000209:etiology; D016015:Logistic Models; D016016:Proportional Hazards Models; D012306:Risk; D016019:Survival Analysis