Symmetric Vaccine Efficacy: Interpretable Estimation and Inference for Vaccine Trials

Traditional measures of vaccine efficacy (VE) are inherently asymmetric, constrained above by 1 but unbounded below. As a result, intervals for VE can extend far below zero, making interpretation challenging and sometimes giving a false impression of evidence that a vaccine is harmful when uncertainty is large. This talk proposes symmetric vaccine efficacy (SVE), a bounded and interpretable alternative to VE that maintains desirable statistical properties while resolving these asymmetries. SVE is defined as a symmetric transformation of observed infection proportions, ensuring estimates remain within (-1, 1) and providing a consistent scale for both beneficial and harmful vaccine effects. We derive a variance and confidence interval for SVE, describe its relationship to traditional VE, and illustrate its application in real trial data. In the example, SVE yields more interpretable uncertainty intervals and clearer graphical summaries compared to standard VE. We then demonstrate open-source tools for computing estimates of SVE and corresponding confidence intervals, available in R through the sve package.

August 4, 2026

9:50 AM – 10:20 AM

ASA Joint Statistical Meeting 2026