title: A Method for Constructing Informative Priors for Bayesian Modeling of Occupational Hygiene Data.
abstract: In many occupational hygiene settings, the demand for more accurate, more precise results is at odds with limited resources. To combat this, practitioners have begun using Bayesian methods to incorporate prior information into their statistical models in order to obtain more refined inference from their data. This is not without risk, however, as incorporating prior information that disagrees with the information contained in data can lead to spurious conclusions, particularly if the prior is too informative. In this article, we propose a method for constructing informative prior distributions for normal and lognormal data that are intuitive to specify and robust to bias. To demonstrate the use of these priors, we walk practitioners through a step-by-step implementation of our priors using an illustrative example. We then conclude with recommendations for general use.
authors.affiliation: Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, PA 19104, USA.
authors.forename: Harrison
authors.initials: H
authors.lastname: Quick
authors.affiliation: Department of Environmental and Occupational Health, Drexel University, Philadelphia, PA 19104, USA.
authors.forename: Tran
authors.initials: T
authors.lastname: Huynh
authors.affiliation: Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
authors.forename: Gurumurthy
authors.initials: G
authors.lastname: Ramachandran
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D016273:Occupational Exposure; D016272:Occupational Health; D018570:Risk Assessment / Q000379:methods; D018401:Sample Size
keywords: decision making; hierarchical modeling; prior sample size; sparse data; truncated priors