Estimating HIV Cross-sectional Incidence Using Recency Tests from a Non-representative Sample

Abstract

Cross-sectional incidence estimation based on recency testing is an important tool in HIV research. This method has been used to estimate “placebo” incidence in active-control HIV prevention trials by applying the cross-sectional estimator to data from the screening population. The application of this approach faces challenges due to non-representative sampling, as individuals aware of their HIV-positive status may be less likely to participate in screening for an HIV prevention trial. To address this, a recent phase 3 trial introduced an test-based exclusion criterion: individuals were excluded during trial screening if they had recently taken an HIV test. To the best of our knowledge, the theoretical and empirical validity of applying a test-based exclusion criterion has yet to be studied. We develop a statistical framework that incorporates non-representative sampling and a testing-based exclusion criterion. We introduce a metric called the effective mean duration of recent infection that mathematically quantifies bias in the recency-based estimate of incidence. We investigate the performance of cross-sectional HIV incidence estimation in settings emulating current trial designs in an extensive simulation study. We find that when HIV negative individuals disproportionately attend screening for prevention trials, the traditional incidence estimator is unreliable unless all individuals with recent HIV tests are excluded from the sample. Additionally, we highlight a trade-off between bias and variability: excluding more individuals reduces bias from non-representative sampling but in many cases increases the variability of incidence estimates (even for a fixed sample size). Our findings emphasize the need for caution when applying the testing-based exclusion criterion and the importance of refining incidence estimation methods to improve the design and analysis of future HIV prevention trials.