Partial Bridging of Vaccine Efficacy to New Populations

TL;DR

This paper proposes a method to estimate vaccine efficacy in new populations using existing trial data and biomarker information, addressing challenges in bridging efficacy results when direct trials are infeasible.

Contribution

It introduces a partial identification approach for marginal efficacy based on bounds of unvaccinated risk, with a nonparametric estimator and confidence bounds.

Findings

Provides a curve of lower bounds for efficacy in new populations.

Develops a nonparametric estimator with confidence bounds.

Applicable to binary interventions and bounded outcomes.

Abstract

Suppose one has data from one or more completed vaccine efficacy trials and wishes to estimate the efficacy in a new setting. Often logistical or ethical considerations make running another efficacy trial impossible. Fortunately, if there is a biomarker that is the primary modifier of efficacy, then the biomarker-conditional efficacy may be identical in the completed trials and the new setting, or at least informative enough to meaningfully bound this quantity. Given a sample of this biomarker from the new population, we might hope we can bridge the results of the completed trials to estimate the vaccine efficacy in this new population. Unfortunately, even knowing the true conditional efficacy in the new population fails to identify the marginal efficacy due to the unknown conditional unvaccinated risk. We define a curve that partially identifies (lower bounds) the marginal efficacy in the new population as a function of the population's marginal unvaccinated risk, under the assumption that one can identify bounds on the conditional unvaccinated risk in the new population. Interpreting the curve only requires identifying plausible regions of the marginal unvaccinated risk in the new population. We present a nonparametric estimator of this curve and develop valid lower confidence bounds that concentrate at a parametric rate. We use vaccine terminology throughout, but the results apply to general binary interventions and bounded outcomes.