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

Laura L. F. Scott¹  George Maldonado²  Igor Burstyn²  Gheorghe Luta² 

[1] Division of Environmental Health Sciences, University of Minnesota School of Public Health, Minneapolis, MN 55455, USA; E-Mail

关键词: probabilistic bias analysis;    Monte Carlo;    disease misclassification;    loss to follow-up;    historical cohort mortality;   

DOI  :  10.3390/ijerph121012834

来源: mdpi

【 摘 要 】

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 (OR_DM-LTF) that compared the mortality of workers with the highest cumulative exposure to those that were considered never-exposed. The geometric mean OR_DM-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 (ε_DM-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 OR_DM-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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.