Abstract
The test-negative design (TND) has become a standard approach for vaccine effectiveness (VE) 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 VE 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 TND with imperfect tests, then developed a bias correction framework for possible misclassification. TND 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.
Highlights
Vaccine effectiveness (VE) is typically estimated as the vaccine-induced risk reduction of the target disease (TD) and has been traditionally studied using cohort or case–control designs
Theoretical studies to date have been based on a limited range of assumptions about efficacy and pathogen epidemiology; it is unclear whether such conclusions hold for all plausible combinations of scenarios
[agd + (1 − b)][(1 − a)d + b] [(1 − a)gd + b][ad + (1 − b)]. This suggests that the influence of sensitivity/specificity on the degree of bias varies depending on the case ratio δ/(1 + δ), i.e. the ratio between the incidence of medical attendance for TD and non-target disease (ND) in the unvaccinated study population3 (Fig. 1)
Summary
Vaccine effectiveness (VE) is typically estimated as the vaccine-induced risk reduction of the target disease (TD) and has been traditionally studied using cohort or case–control designs. TND studies are often considered to be special cases of case–control studies, they are free from the issue of differential recruitment because the recruitment and classification are mutually-independent [14] This means that, while Greenland’s method does not apply to TND as-is, another type of bias correction may still be possible. Previous analysis of misclassification bias has not considered the impact of multivariate analysis, where potential confounders (e.g. age and sex) are included in the model used to estimate VE To address these issues, we develop a bias correction method for the test-negative VE studies that uses only data commonly available in field studies. We develop a bias correction method for the test-negative VE studies that uses only data commonly available in field studies We apply these methods to multivariate analyses. We evaluate the performance of our methods by simulations of plausible epidemiological scenarios
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