Estimating incidence rates with misclassified disease status: a likelihood-based approach, with application to...

In epidemiologic research, incidence is often estimated from data arising from an imperfect diagnostic test performed at unequally spaced intervals over time.

We developed a likelihood-based method to estimate incidence when disease status is measured imperfectly and assays are performed at multiple unequally spaced visits. We assumed conditional independence, no remission, known constant levels of sensitivity and specificity, and constant incidence rates over time. The method performance was evaluated by examining its bias, accuracy (i.e., mean squared error (MSE)), and coverage probability in a simulation study of 4000 datasets, and then we applied the proposed method to a study of hepatitis C virus (HCV) infection in a cohort of pregnant women in the period 1997-2006.

The simulation revealed that our method has minimal bias and low MSE, as well as good coverage probability of the resulting confidence intervals. In the application to HCV study, the standard incidence rate estimate which ignores the imperfections of the diagnostic test (number of events/person-years), was 13.7 new HCV cases per 1000 person-years (95% confidence interval 10.1, 17.4). The adjusted incidence estimates (obtained using our proposed method) ranged from 0.4 cases per 1000 person-years (when sensitivity and specificity were assumed to both be 95%) to 13.7 cases per 1000 person-years (when sensitivity and specificity were both 100%). The magnitude of difference between standard and adjusted estimates varied depending on specificity and sensitivity assumptions. Specificity had the greatest impact on the magnitude of bias.

Scientists should be aware of the impact of misclassification on incidence estimates. Appropriate study design, proper selection of the diagnostic test, and adjustment for misclassification probabilities in the analysis is necessary to obtain the most accurate incidence estimates.