  The advent and subsequent widespread availability of preventive vaccines has
altered the course of public health over the past century. Despite this
success, effective vaccines to prevent many high-burden diseases, including
HIV, have been slow to develop. Vaccine development can be aided by the
identification of immune response markers that serve as effective surrogates
for clinically significant infection or disease endpoints. However, measuring
immune response marker activity is often costly, which has motivated the usage
of two-phase sampling for immune response evaluation in clinical trials of
preventive vaccines. In such trials, the measurement of immunological markers
is performed on a subset of trial participants, where enrollment in this second
phase is potentially contingent on the observed study outcome and other
participant-level information. We propose nonparametric methodology for
efficiently estimating a counterfactual parameter that quantifies the impact of
a given immune response marker on the subsequent probability of infection.
Along the way, we fill in theoretical gaps pertaining to the asymptotic
behavior of nonparametric efficient estimators in the context of two-phase
sampling, including a multiple robustness property enjoyed by our estimators.
Techniques for constructing confidence intervals and hypothesis tests are
presented, and an open source software implementation of the methodology, the
txshift R package, is introduced. We illustrate the proposed techniques using
data from a recent preventive HIV vaccine efficacy trial.
