Bias due to differential and non-differential disease- and exposure misclassification in studies of vaccine effectiveness
Background Studies of vaccine effectiveness (VE) rely on accurate identification of vaccination and cases of vaccine-preventable disease. In practice, diagnostic tests, clinical case definitions and vaccination records often present inaccuracies, leading to biased VE estimates. Previous studies investigated the impact of non-differential disease misclassification on VE estimation. Methods We explored, through simulation, the impact of non-differential and differential disease- and exposure misclassification when estimating VE using cohort, case-control, test-negative case-control and case-cohort designs. The impact of misclassification on the estimated VE is demonstrated for VE studies on childhood seasonal influenza and pertussis vaccination. We additionally developed a web-application graphically presenting bias for user-selected parameters. Results Depending on the scenario, the misclassification parameters had differing impacts. Decreased exposure specificity had greatest impact for influenza VE estimation when vaccination coverage was low. Decreased exposure sensitivity had greatest impact for pertussis VE estimation for which high vaccination coverage is typically achieved. The impact of the exposure misclassification parameters was found to be more noticeable than that of the disease misclassification parameters. When misclassification is limited, all study designs perform equally. In case of substantial (differential) disease misclassification, the test-negative design performs worse. Conclusions Misclassification can lead to significant bias in VE estimates and its impact strongly depends on the scenario. We developed a web-application for assessing the potential (joint) impact of possibly differential disease- and exposure misclassification that can be modified by users to their own study scenario. Our results and the simulation tool may be used to guide better design, conduct and interpretation of future VE studies.
|Keywords||Research Article, Biology and Life Sciences, Immunology, Vaccination and Immunization, Medicine and Health Sciences, Immunology, Vaccination and Immunization, Medicine and Health Sciences, Public and Occupational Health, Preventive Medicine, Vaccination and Immunization, Medicine and Health Sciences, Infectious Diseases, Infectious Disease Control, Vaccines, Medicine and Health Sciences, Infectious Diseases, Viral Diseases, Influenza, Medicine and Health Sciences, Infectious Diseases, Bacterial Diseases, Pertussis, Medicine and Health Sciences, Pediatrics, Research and Analysis Methods, Simulation and Modeling, Medicine and Health Sciences, Pathology and Laboratory Medicine, Pathogens, Medicine and Health Sciences, Epidemiology|
|Publisher||Public Library of Science|
|Persistent URL||dx.doi.org/10.1371/journal.pone.0199180, hdl.handle.net/1765/113875|
de Smedt, T, Merrall, E, Macina, D, Pérez-Vilar, S, Andrews, N.J, & Bollaerts, K. (2018). Bias due to differential and non-differential disease- and exposure misclassification in studies of vaccine effectiveness. PLoS ONE. doi:10.1371/journal.pone.0199180