Estimation and inference for case-control studies with multiple non-gold standard exposure assessments: with an occupational health application.
In occupational case-control studies, work-related exposure assessments are often fallible measures of the true underlying exposure. In lieu of a gold standard, often more than 2 imperfect measurements (e.g. triads) are used to assess exposure. While methods exist to assess the diagnostic accuracy in the absence of a gold standard, these methods are infrequently used to correct for measurement error in exposure-disease associations in occupational case-control studies. Here, we present a likelihood-based approach that (a) provides evidence regarding whether the misclassification of tests is differential or nondifferential; (b) provides evidence whether the misclassification of tests is independent or dependent conditional on latent exposure status, and (c) estimates the measurement error-corrected exposure-disease association. These approaches use information from all imperfect assessments simultaneously in a unified manner, which in turn can provide a more accurate estimate of exposure-disease association than that based on individual assessments. The performance of this method is investigated through simulation studies and applied to the National Occupational Hazard Survey, a case-control study assessing the association between asbestos exposure and mesothelioma.
Chu
Haitao
H
Department of Biostatistics and Lineberger Comprehensive Cancer Center, The University of North Carolina, Chapel Hill, NC 27599, USA. hchu@bios.unc.edu
Cole
Stephen R
SR
Wei
Ying
Y
Ibrahim
Joseph G
JG
D001194:Asbestos / Q000008:administration & dosage / Q000009:adverse effects; D056808:Biostatistics / Q000379:methods*; D016022:Case-Control Studies; D016001:Confidence Intervals; D003625:Data Collection; D006801:Humans; D016013:Likelihood Functions; D008654:Mesothelioma / Q000209:etiology; D015233:Models, Statistical; D016273:Occupational Exposure / Q000592:standards / Q000706:statistics & numerical data*; D016272:Occupational Health / Q000706:statistics & numerical data; D014481:United States
D001194:Asbestos