Negative Control Methods to De-bias Test-Negative Design Studies of COVID-19 Vaccine Effectiveness
Wednesday, January 17, 2024
2:30 PM-3:30 PM
The Department of Epidemiology and Biostatistics Seminar Series welcomes Eric J. Tchetgen Tchetgen, PhD,
University Professor,
Professor of Biostatistics,
Professor of Statistics in Data Science,
University of Pennsylvania, who will present "Negative Control Methods to De-bias Test-Negative Design Studies of
COVID-19 Vaccine Effectiveness."
The test-negative design (TND) has become a standard approach to evaluate
vaccine effectiveness against the risk of acquiring infectious diseases in
real-world settings, such as COVID-19. In a TND study, individuals who
experience symptoms and seek care are recruited and tested for the infectious
disease which defines cases and controls. Despite TND's potential to reduce
unobserved differences in healthcare seeking behavior (HSB) between vaccinated
and unvaccinated subjects, it remains subject to potential biases. First,
residual confounding bias may remain due to unobserved HSB, occupation as
healthcare worker, or previous infection history. Second, because selection
into the TND sample is a common consequence of infection and HSB, collider
stratification bias may exist when conditioning the analysis on COVID testing,
which further induces confounding by latent HSB. In this paper, we present a
novel approach to identify and estimate vaccine effectiveness in the target
population by carefully leveraging a pair of negative control exposure and
outcome variables to account for potential hidden bias in TND studies. We
illustrate our proposed method with extensive simulation and an application to
study COVID-19 vaccine effectiveness using data from the University of Michigan
Health System.
Eric J. Tchetgen Tchetgen is The University Professor, Professor of
Biostatistics at the Perelman School of Medicine and Professor of Statistics
and Data Science at The Wharton School at the University of Pennsylvania. He
co-directs the Penn Center for Causal Inference, which supports the development
and dissemination of causal inference methods in Health and Social Sciences. He
has published extensively on Causal Inference, Missing Data and Semiparametric
Theory with several impactful applications ranging from HIV research, Genetic
Epidemiology, Environmental Health and Alzheimer's Disease and related aging
disorders. He is an Amazon scholar working with Amazon scientists on a variety
of causal inference problems in the Tech industry space. Professor Tchetgen
Tchetgen is an 2022 inaugural co-recipient of the newly established Rousseeuw
Prize for statistics in recognition for his work in Causal Inference with
applications in Medicine and Public Health.
For more information, please email nanderson@drexel.edu.
Contact Information
Nancy Colon-Anderson
nanderson@drexel.edu