Journal article
Bayesian Method for Improving Logistic Regression Estimates under Group-Based Exposure Assessment with Additive Measurement Errors
Archives of environmental & occupational health, v 64(4), pp 261-265
30 Nov 2009
PMID: 20007122
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The group-based exposure assessment has been widely used in occupational epidemiology. When the sample size used to estimate group means is "large", this leads to negligible attenuation in the estimation of odds ratio. However, the bias is proportional to the between-subject variability and is affected by the difference in true group means. We explore a Bayesian method, which adjusts in a natural way for the extra uncertainty in the outcome model associated with using the predicted values as exposures. We aim to improve the estimate obtained in naïve analysis by exploiting the properties of Berkson type error structure. We consider cases where differences in the proximity of group means and the between-subject variance are both large. The results of the simulations show that our Bayesian measurement error adjustment method that follows group-based exposure assessment improves estimates of odds ratios when the between-subject variance is large and group means are far apart.
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Details
- Title
- Bayesian Method for Improving Logistic Regression Estimates under Group-Based Exposure Assessment with Additive Measurement Errors
- Creators
- Hyang-Mi Kim - University of CalgaryIgor Burstyn - University of Alberta
- Publication Details
- Archives of environmental & occupational health, v 64(4), pp 261-265
- Publisher
- Taylor & Francis Group
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Environmental and Occupational Health
- Web of Science ID
- WOS:000273314700006
- Scopus ID
- 2-s2.0-74049110956
- Other Identifier
- 991019203712604721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Environmental Sciences
- Public, Environmental & Occupational Health