Correction to: Bayesian Analysis of Occupational Exposure Data with Conjugate Priors

Details

• Personal Author:
  Burstyn, Igor ; Jones, Rachel M.
• Description:
  Bayesian analysis is a flexible method that can yield insight into occupational exposures as the methods quantify plausible values for exposure parameters of interest, such as the mean, variance, and specific percentiles of the exposure distribution. We describe three Bayesian analysis methods for the analysis of normally distributed data (e.g. the logarithm of measurements of chemical hazards) that use conjugate prior distributions (normal for the mean, and inverse-x2, inverse-r, or vague for the variance) to provide analytical expressions for the posterior distributions of the sufficient statistics of the normal distribution (e.g. the mean and variance). From these posterior distributions, the posterior distribution of any parameter of interest about the exposure distribution can be tabulated. The methods are illustrated using lead exposure data collected by the Occupational Safety and Health Administration at a copper foundry on multiple occasions. A unique feature of the normal-inverse-r method is that dependence of the mean and variance prior distributions is integrated out of the posterior distributions expressions, suggesting that a 'default' prior distribution on variance may be used: candidate default distributions are proposed based on the literature. Relative to other Bayesian analysis methods used in industrial hygiene, the methods described are flexible, and can be implemented without specialized software. Correction https://doi.org/10.1093/annweh/wxae065: This correction addresses an error in the equations and computer code related to Method 2 presentation in the original article. The below details have been corrected only in this correction notice to preserve the published version of record. Correction https://doi.org/10.1093/annweh/wxae065: This correction addresses an error in the equations and computer code related to Method 2 presentation in the original article. [Description provided by NIOSH]
• Subjects:
  Occupational Exposure Occupational Health Safety
• Keywords:
  Author Keywords: Bayesian Analysis; Industrial Hygiene; Occupational Exposures; Prior Distributions Statistical Analysis; Exposure Assessment; Data Processing; Analytical Processes; Analytical Methods; Occupational Exposure;
• ISSN:
  2398-7308
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Illinois ; OSHA Region 3 ; OSHA Region 5 ; Pennsylvania
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  4 pdf pages
• Volume:
  61
• Issue:
  5
• NIOSHTIC Number:
  nn:20053036
• Citation:
  Ann Work Expo Health 2017 Jun; 61(5):504-514
• Contact Point Address:
  Rachael M. Jones, School of Public Health, University of Illinois at Chicago, 2121 W Taylor Street, Chicago, IL 60612 USA
• Email:
  rjones25@uic.edu
• Federal Fiscal Year:
  2017
• Performing Organization:
  University of Illinois at Chicago
• Peer Reviewed:
  True
• Start Date:
  20140401
• Source Full Name:
  Annals of Work Exposures and Health
• End Date:
  20170331
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:73308172a54b3a345fef4ac8f248fb03e89f1c29ed35db917f069597557216ca9155032f1c3b56ba7b2a9fe8a2e82c484171e551e0c776b3aa88667f4cc392ce
• Download URL:
  https://stacks.cdc.gov/view/cdc/231533/cdc_231533_DS1.pdf
• File Type:
  [PDF - 575.90 KB ]