ArticleStatistics in medicine2022
Statistical considerations for cross-sectional HIV incidence estimation based on recency test.
Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
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Who cites it
9 citing papers in PubMed, 1 citations in OpenAlex.
- HIV Prevention Among Men Who Have Sex With Men: Tenofovir Alafenamide Combination Preexposure Prophylaxis Versus Placebo.The Journal of infectious diseases · 2024Trial
- A Population-Based HIV Vaccine Efficacy Trial Design: Statistical Framework and Design Considerations.Vaccines · 2026Review
- Comparing Approaches for Estimating Counterfactual HIV Incidence Among Populations With High Vulnerability to HIV in Lima, Peru: A Multi-Study Comparative Analysis.Journal of the International AIDS Society · 2026Article
- Addressing Population Heterogeneity for HIV Incidence Estimation Based on Recency Test.Statistics in medicine · 2025Article
- Bridged treatment comparisons: an illustrative application in HIV treatment.American journal of epidemiology · 2025Article
- An enhanced cross-sectional HIV incidence estimator that incorporates prior HIV test results.Statistics in medicine · 2024Article
- Fusing trial data for treatment comparisons: Single vs multi-span bridging.Statistics in medicine · 2024Article
- Likelihood-based inferences for active-arm trial with counterfactual incidence based on recency assay.Statistical communications in infectious diseases · 2024Article
- Recent HIV Infection: Diagnosis and Public Health Implications.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
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 article, we present a theoretical framework with which to understand these estimators and interrogate their assumptions, and perform a simulation study and data analysis to assess the performance of these estimators under realistic HIV epidemiological dynamics. We find that the snapshot estimator and the adjusted estimator perform well when their corresponding assumptions hold. When assumptions on constant incidence and recency test characteristics fail to hold, the adjusted estimator is more robust than the snapshot estimator. 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.