Bayesian Correction for Exposure Misclassification and Evolution of Evidence in Two Studies of the Association Between Maternal Occupational Exposure to Asthmagens and Risk of Autism Spectrum Disorder

Current Environmental Health Reports - Tập 5 - Trang 338-350 - 2018
Alison B. Singer1,2,3, M. Daniele Fallin1,2,4, Igor Burstyn5,6,7
1Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, USA
2Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins University Bloomberg School of Public Health, Baltimore, USA
3Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, USA
4Department of Mental Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, USA
5Department of Environmental and Occupational Health, Drexel University Dornsife School of Public Health, Philadelphia, USA
6Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, USA
7A.J. Drexel Autism Institute, Drexel University Dornsife School of Public Health, Philadelphia, USA

Tóm tắt

Inference in epidemiologic studies is plagued by exposure misclassification. Several methods exist to correct for misclassification error. One approach is to use point estimates for the sensitivity (Sn) and specificity (Sp) of the tool used for exposure assessment. Unfortunately, we typically do not know the Sn and Sp with certainty. Bayesian methods for exposure misclassification correction allow us to model this uncertainty via distributions for Sn and Sp. These methods have been applied in epidemiologic literature, but are not considered a mainstream approach, especially in occupational epidemiology. Here, we illustrate an occupational epidemiology application of a Bayesian approach to correct for the differential misclassification error generated by estimating occupational exposures from job codes using a job exposure matrix (JEM). We argue that analyses accounting for exposure misclassification should become more commonplace in the literature.

Tài liệu tham khảo

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