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 (εDM-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.
Keywords: Monte Carlo; disease misclassification; historical cohort mortality; loss to follow-up; probabilistic bias analysis.