title: Model uncertainty and risk estimation for experimental studies of quantal responses.
abstract: Experimental animal studies often serve as the basis for predicting risk of adverse responses in humans exposed to occupational hazards. A statistical model is applied to exposure-response data and this fitted model may be used to obtain estimates of the exposure associated with a specified level of adverse response. Unfortunately, a number of different statistical models are candidates for fitting the data and may result in wide ranging estimates of risk. Bayesian model averaging (BMA) offers a strategy for addressing uncertainty in the selection of statistical models when generating risk estimates. This strategy is illustrated with two examples: applying the multistage model to cancer responses and a second example where different quantal models are fit to kidney lesion data. BMA provides excess risk estimates or benchmark dose estimates that reflects model uncertainty.
authors.affiliation: Department of Mathematics and Statistics, Miami University, Oxford, OH 45056, USA. bailerj@muohio.edu
authors.forename: A John
authors.initials: AJ
authors.lastname: Bailer
authors.forename: Robert B
authors.initials: RB
authors.lastname: Noble
authors.forename: Matthew W
authors.initials: MW
authors.lastname: Wheeler
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D035843:Biomedical Research / Q000379:methods; D003627:Data Interpretation, Statistical; D004195:Disease Models, Animal; D004305:Dose-Response Relationship, Drug; D019855:Ethylene Glycol / Q000009:adverse effects; D006801:Humans; D007684:Kidney Tubules / Q000187:drug effects; D008223:Lymphoma / Q000473:pathology; D051379:Mice; D015233:Models, Statistical; D008962:Models, Theoretical; D009369:Neoplasms / Q000473:pathology; D016273:Occupational Exposure; D011336:Probability; D051381:Rats; D012306:Risk; D018570:Risk Assessment
chemical_list: D019855:Ethylene Glycol