A Method for Constructing Informative Priors for Bayesian Modeling of Occupational Hygiene Data. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Quick, Harrison; Huynh, Tran; Ramachandran, Gurumurthy
• Is Part Of: Annals of work exposures and health. Volume 61:Issue 1(2017)
• Page Start: 67
• Page End: 75
• Publication Date: 2017-01-01
• Subjects: decision making -- hierarchical modeling -- prior sample size -- sparse data -- truncated priors; Medicine, Industrial -- Periodicals; Industrial hygiene -- Periodicals; 613.6205
• DOI: 10.1093/annweh/wxw001
• Languages: English
• ISSNs: 2398-7316

Abstract

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.