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.

翻译：基于近期感染检测的横断面发病率估计已成为HIV研究中的一项重要工具。近年来，该方法在HIV预防试验中愈发受到重视，用于估计参与者在未接受预防性治疗时可能经历的"安慰剂"发病率。然而，由于样本的非代表性，该方法的应用面临挑战——已知自身HIV阳性状态的个体可能更少参与HIV预防试验的筛查。为解决此问题，一项近期开展的3期临床试验根据个体是否接受过近期HIV检测来排除参与者。据我们所知，该方法的有效性尚未得到充分研究。在本工作中，我们探究了在实际试验场景中，基于既往HIV检测结果排除个体时，横断面HIV发病率估计的性能表现。我们建立了一个统计框架，该框架整合了基于检测的排除标准以及可能的非代表性抽样。我们引入了一个称为"有效近期感染平均持续时间"的指标，该指标从数学上量化了发病率估计中的偏差。我们开展了广泛的模拟研究，以评估不同情境下发病率估计器的性能。研究结果表明，当筛查参与率受到HIV感染认知状态影响时，除非排除所有接受过近期HIV检测的个体，否则发病率估计将变得不可靠。此外，我们发现偏差与变异性之间存在权衡关系：排除更多个体可降低非代表性抽样带来的偏差，但在多数情况下会增加发病率估计的变异性。这些发现提示我们在应用基于检测的排除标准时需要保持谨慎，并强调了改进发病率估计方法对于优化未来HIV预防试验设计与评估的重要性。