title: Bayesian multimodel inference for dose-response studies.
abstract: Statistical inference in dose-response studies is model-based: The analyst posits a mathematical model of the relation between exposure and response, estimates parameters of the model, and reports conclusions conditional on the model. Such analyses rarely include any accounting for the uncertainties associated with model selection. The Bayesian inferential system provides a convenient framework for model selection and multimodel inference. In this paper we briefly describe the Bayesian paradigm and Bayesian multimodel inference. We then present a family of models for multinomial dose-response data and apply Bayesian multimodel inferential methods to the analysis of data on the reproductive success of American kestrels (Falco sparveriuss) exposed to various sublethal dietary concentrations of methylmercury.
authors: U.S. Geological Survey Patuxent Wildlife Research Center, Laurel, Maryland 20708, USA. wlink@usgs.gov
authors: William A
authors: WA
authors: Link
authors: Peter H
authors: PH
authors: Albers
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D004305:Dose-Response Relationship, Drug; D046093:Falconiformes / Q000502:physiology; D008767:Methylmercury Compounds / Q000633:toxicity; D008954:Models, Biological; D011336:Probability; D012098:Reproduction / Q000187:drug effects
chemical_list: D008767:Methylmercury Compounds