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A Method for Constructing Informative Priors for Bayesian Modeling of Occupational Hygiene Data
Journal article   Peer reviewed

A Method for Constructing Informative Priors for Bayesian Modeling of Occupational Hygiene Data

Harrison Quick, Tran Huynh and Gurumurthy Ramachandran
Annals of work exposures and health, v 61(1), pp 67-75
01 Jan 2017
DOI: https://doi.org/10.1093/annweh/wxw001
PMID: 28395307
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Bayes Theorem Computer Simulation Data Interpretation, Statistical Humans Models, Statistical Occupational Exposure Occupational Health Risk Assessment - methods Sample Size
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.

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5 Record Views
14 citations in Web of Science
15 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

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Collaboration types
Domestic collaboration
Web of Science research areas
Public, Environmental & Occupational Health
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