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

TL;DR

This study evaluates how non-representative sampling and testing-based exclusions affect the accuracy of cross-sectional HIV incidence estimates, proposing a statistical framework and highlighting the bias-variance trade-off in such methods.

Contribution

The paper introduces a new statistical framework and a metric called effective mean duration of recent infection to assess bias in HIV incidence estimation under non-representative sampling.

Findings

Excluding individuals with recent HIV tests reduces bias but increases variability.

Knowledge of HIV status influences screening attendance, affecting estimator reliability.

Refining incidence estimation methods is crucial for future HIV prevention trial design.

Abstract

Cross-sectional incidence estimation based on recency testing has become a widely used tool in HIV research. Recently, this method has gained prominence in HIV prevention trials to estimate the "placebo" incidence that participants might experience without preventive treatment. 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 excluded individuals based on whether they have had a recent HIV test. To the best of our knowledge, the validity of this approach has yet to be studied. In our work, we investigate the performance of cross-sectional HIV incidence estimation when excluding individuals based on prior HIV tests in realistic trial settings. We develop a statistical framework that incorporates a testing-based criterion and possible non-representative sampling. We introduce a metric we call the effective mean duration of recent infection (MDRI) that mathematically quantifies bias in incidence estimation. We conduct an extensive simulation study to evaluate incidence estimator performance under various scenarios. Our findings reveal that when screening attendance is affected by knowledge of HIV status, incidence estimators become unreliable unless all individuals with recent HIV tests are excluded. Additionally, we identified 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. These findings highlight the need for caution when applying testing-based criteria and emphasize the importance of refining incidence estimation methods to improve the design and evaluation of future HIV prevention trials.