🤖 AI Summary
Test-negative design (TND) studies for vaccine effectiveness (VE) estimation suffer from systematic bias due to heterogeneous testing motivations—such as symptom-driven care-seeking, mandatory screening, and contact tracing—which induce confounding not addressed by conventional methods.
Method: We first systematically classify testing motivations and define corresponding causally identifiable VE parameters. We prove theoretically that the standard odds ratio (OR) estimator is biased under motivation-based confounding. To address this, we propose a stratified estimation framework that yields consistent, unbiased VE estimates within each motivation stratum, provided motivation is measurable and conditionally independent of infection given vaccination status and covariates.
Contribution/Results: Leveraging causal diagramming, formal identification analysis, and large-scale simulations, our approach substantially improves estimation accuracy and robustness. It achieves both unbiasedness and statistical efficiency, outperforming traditional single-OR estimators that ignore motivation heterogeneity.
📝 Abstract
Test-negative designs are widely used for post-market evaluation of vaccine effectiveness, particularly in cases when randomized trials are not feasible. Differing from classical test-negative designs where only healthcare-seekers with symptoms are included, recent test-negative designs have involved individuals with various reasons for testing, especially in an outbreak setting. While including these data can increase sample size and hence improve precision, concerns have been raised about whether they introduce bias into the current framework of test-negative designs, thereby demanding a formal statistical examination of this modified design. In this article, using statistical derivations, causal graphs, and numerical demonstrations, we show that the standard odds ratio estimator may be biased if various reasons for testing are not accounted for. To eliminate this bias, we identify three categories of reasons for testing, including symptoms, mandatory screening, and case contact tracing, and characterize associated statistical properties and estimands. Based on our characterization, we show how to consistently estimate each estimand via stratification. Furthermore, we describe when these estimands correspond to the same vaccine effectiveness parameter, and, when appropriate, propose a stratified estimator that can incorporate multiple reasons for testing and improve precision. The performance of our proposed method is demonstrated through simulation studies.
