title: Integrating informative priors from experimental research with Bayesian methods: an example from radiation epidemiology.
abstract: Informative priors can be a useful tool for epidemiologists to handle problems of sparse data in regression modeling. It is sometimes the case that an investigator is studying a population exposed to two agents, X and Y, where Y is the agent of primary interest. Previous research may suggest that the exposures have different effects on the health outcome of interest, one being more harmful than the other. Such information may be derived from epidemiologic analyses; however, in the case where such evidence is unavailable, knowledge can be drawn from toxicologic studies or other experimental research. Unfortunately, using toxicologic findings to develop informative priors in epidemiologic analyses requires strong assumptions, with no established method for its utilization. We present a method to help bridge the gap between animal and cellular studies and epidemiologic research by specification of an order-constrained prior. We illustrate this approach using an example from radiation epidemiology.
authors.affiliation: Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7435, USA. ghassan.hamra@unc.edu
authors.forename: Ghassan
authors.initials: G
authors.lastname: Hamra
authors.forename: David
authors.initials: D
authors.lastname: Richardson
authors.forename: Richard
authors.initials: R
authors.lastname: Maclehose
authors.forename: Steve
authors.initials: S
authors.lastname: Wing
mesh_terms: D001499:Bayes Theorem; D001610:Beta Particles / Q000009:adverse effects; D004307:Dose-Response Relationship, Radiation; D015340:Epidemiologic Research Design; D005720:Gamma Rays / Q000009:adverse effects; D006801:Humans; D007953:Leukemia, Radiation-Induced / Q000209:etiology / Q000401:mortality; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D009784:Occupational Diseases / Q000209:etiology / Q000401:mortality; D016273:Occupational Exposure / Q000009:adverse effects; D012044:Regression Analysis; D014116:Toxicology; D014316:Tritium / Q000633:toxicity
chemical_list: D014316:Tritium