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Article

Quantifying and Adjusting for Disease Misclassification Due to Loss to Follow-Up in Historical Cohort Mortality Studies

Division of Environmental Health Sciences, University of Minnesota School of Public Health, Minneapolis, MN 55455, USA
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Academic Editors: Igor Burstyn, Gheorghe Luta and Paul B. Tchounwou
Int. J. Environ. Res. Public Health 2015, 12(10), 12834-12846; https://doi.org/10.3390/ijerph121012834
Received: 16 March 2015 / Revised: 22 September 2015 / Accepted: 8 October 2015 / Published: 15 October 2015
(This article belongs to the Special Issue Methodological Innovations and Reflections-1)
The purpose of this analysis was to quantify and adjust for disease misclassification from loss to follow-up in a historical cohort mortality study of workers where exposure was categorized as a multi-level variable. Disease classification parameters were defined using 2008 mortality data for the New Zealand population and the proportions of known deaths observed for the cohort. The probability distributions for each classification parameter were constructed to account for potential differences in mortality due to exposure status, gender, and ethnicity. Probabilistic uncertainty analysis (bias analysis), which uses Monte Carlo techniques, was then used to sample each parameter distribution 50,000 times, calculating adjusted odds ratios (ORDM-LTF) that compared the mortality of workers with the highest cumulative exposure to those that were considered never-exposed. The geometric mean ORDM-LTF ranged between 1.65 (certainty interval (CI): 0.50–3.88) and 3.33 (CI: 1.21–10.48), and the geometric mean of the disease-misclassification error factor (eDM-LTF), which is the ratio of the observed odds ratio to the adjusted odds ratio, had a range of 0.91 (CI: 0.29–2.52) to 1.85 (CI: 0.78–6.07). Only when workers in the highest exposure category were more likely than those never-exposed to be misclassified as non-cases did the ORDM-LTF frequency distributions shift further away from the null. The application of uncertainty analysis to historical cohort mortality studies with multi-level exposures can provide valuable insight into the magnitude and direction of study error resulting from losses to follow-up. View Full-Text
Keywords: probabilistic bias analysis; Monte Carlo; disease misclassification; loss to follow-up; historical cohort mortality probabilistic bias analysis; Monte Carlo; disease misclassification; loss to follow-up; historical cohort mortality
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MDPI and ACS Style

Scott, L.L.F.; Maldonado, G. Quantifying and Adjusting for Disease Misclassification Due to Loss to Follow-Up in Historical Cohort Mortality Studies. Int. J. Environ. Res. Public Health 2015, 12, 12834-12846. https://doi.org/10.3390/ijerph121012834

AMA Style

Scott LLF, Maldonado G. Quantifying and Adjusting for Disease Misclassification Due to Loss to Follow-Up in Historical Cohort Mortality Studies. International Journal of Environmental Research and Public Health. 2015; 12(10):12834-12846. https://doi.org/10.3390/ijerph121012834

Chicago/Turabian Style

Scott, Laura L.F., and George Maldonado. 2015. "Quantifying and Adjusting for Disease Misclassification Due to Loss to Follow-Up in Historical Cohort Mortality Studies" International Journal of Environmental Research and Public Health 12, no. 10: 12834-12846. https://doi.org/10.3390/ijerph121012834

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