A simple and powerful test of vaccine waning

TL;DR

This paper introduces a powerful, interpretable statistical test to detect vaccine efficacy waning over time, demonstrating increased power over existing methods through real and simulated data analyses.

Contribution

It proposes a new formal test for vaccine waning that is more powerful and interpretable, applicable with summary data from clinical trials, and provides bounds on waning effects.

Findings

The test shows increased power compared to previous methods.

Reanalysis of COVID-19 vaccine data indicates waning efficacy.

The approach is valid under weaker, interpretable assumptions.

Abstract

Determining whether vaccine efficacy wanes is important for individual and public decision making. Yet, quantification of waning is a subtle task. The classical approaches cannot be interpreted as measures of declining efficacy unless we impose unreasonable assumptions. Recently, formal causal estimands designed to quantify vaccine waning have been proposed. These estimands can be bounded under weaker assumptions, but the bounds are often too wide to make claims about the presence of waning. We propose a different approach: a formal test to assess whether a treatment effect is constant over time at the individual level. This test provides a considerable power gain over existing approaches and is valid under interpretable assumptions in vaccine trials. We illustrate the increase in power through real and simulated examples, using three different approaches to compute the test statistics. Two of these approaches are based solely on summary data, accessible from existing clinical trials. Beyond our test, we also give new results that bound the waning effect. We use our methods to reanalyze data from a randomized controlled trial of the BNT162b2 COVID-19 vaccine. While prior analysis did not establish waning, our test rejects the null hypothesis of no waning.