Title: Partial Bridging of Vaccine Efficacy to New Populations,
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.