Statistical Considerations for Cross-Sectional HIV Incidence Estimation Based on Recency Test

TL;DR

This paper develops a theoretical framework and simulation analysis to evaluate the statistical properties of recency test-based estimators for HIV incidence from cross-sectional data, addressing their assumptions and practical performance.

Contribution

It provides a rigorous statistical analysis of existing estimators, highlighting their assumptions, limitations, and offering recommendations for their use in HIV incidence estimation.

Findings

Existing estimators have specific assumptions that may not hold in practice.

Simulation results reveal biases and variances under different epidemiological scenarios.

Recommendations improve the reliability of HIV incidence estimates using recency tests.

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 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.