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

A Endo ORCID logo; S Funk ORCID logo; AJ Kucharski ORCID logo; (2020) Bias correction methods for test-negative designs in the presence of misclassification. EPIDEMIOLOGY AND INFECTION, 148. e216-. ISSN 0950-2688 DOI: 10.1017/S0950268820002058
Copy

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.


picture_as_pdf
Bias correction methods for test-negative designs in the presence of misclassification.pdf
subject
Published Version
Available under Creative Commons: NC-ND 3.0

View Download

Atom BibTeX OpenURL ContextObject in Span Multiline CSV OpenURL ContextObject Dublin Core Dublin Core MPEG-21 DIDL EndNote HTML Citation JSON MARC (ASCII) MARC (ISO 2709) METS MODS RDF+N3 RDF+N-Triples RDF+XML RIOXX2 XML Reference Manager Refer Simple Metadata ASCII Citation EP3 XML
Export

Downloads