Test-negative Designs with Various Reasons for Testing: Statistical Bias and Solution

Test-negative designs (TNDs) are widely used for postmarket evaluation of vaccine effectiveness (VE), particularly in cases when randomized trials are not feasible. Unlike classical TNDs, which only include healthcare seekers with symptoms, recent TNDs 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 TNDs, 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 taken into account. To eliminate this bias, we identify three categories of reasons for testing, namely 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 VE parameter and, when appropriate, propose a stratified estimator that can incorporate multiple reasons for testing and improve precision. We demonstrate the performance of our proposed method through simulation studies and a real-data analysis.