Generalized conjugate priors for Bayesian analysis of risk and survival regressions
Authors:
Greenland Sander
Affiliation:
Department of Epidemiology, UCLA School of Public Health, 22333 Swenson Drive, Topanga, California 90290, USA. lesdomes@ucla.edu
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.