  Longitudinal cohorts to determine the incidence of HIV infection are
logistically challenging, so researchers have sought alternative strategies.
Recency test methods use biomarker profiles of HIV-infected subjects in a
cross-sectional sample to infer whether they are "recently" infected and to
estimate incidence in the population. Two main estimators have been used in
practice: one that assumes a recency test is perfectly specific, and another
that allows for false-recent results. To date, these commonly used estimators
have not been rigorously studied with respect to their assumptions and
statistical properties. In this paper, we present a theoretical framework with
which to understand these estimators and interrogate their assumptions, and
perform a simulation study to assess the performance of these estimators under
realistic HIV epidemiological dynamics. We conclude with recommendations for
the use of these estimators in practice and a discussion of future
methodological developments to improve HIV incidence estimation via recency
test.
