Bias correction methods for test-negative designs in the presence of misclassification

Abstract

The test-negative design has become a standard approach for vaccine effectiveness studies. However, previous studies suggested that it may be more vulnerable than other designs to misclassification of disease outcome caused by imperfect diagnostic tests. This could be a particular limitation in vaccine effectiveness studies where simple tests (e.g. rapid influenza diagnostic tests) are used for logistical convenience. To address this issue, we derived a mathematical representation of the test-negative design with imperfect tests, then developed a bias correction framework for possible misclassification. Test-negative design studies usually include multiple covariates other than vaccine history to adjust for potential confounders; our methods can also address multivariate analyses and be easily coupled with existing estimation tools. We validated the performance of these methods using simulations of common scenarios for vaccine efficacy and were able to obtain unbiased estimates in a variety of parameter settings.