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Mixture models for calibrating the BED for HIV incidence testing.

Severin Guy Mahiane, Agnès Fiamma, Bertran Auvert

    Statistics in Medicine
    |May 17, 2014
    PubMed
    Summary

    New statistical methods improve Human Immunodeficiency Virus (HIV) incidence estimation using antibody biomarkers. These approaches accurately determine recent infections, aiding public health surveillance and intervention strategies.

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    Area of Science:

    • Epidemiology
    • Biostatistics
    • Immunology

    Background:

    • Antibody biomarkers are crucial for differentiating recent from established Human Immunodeficiency Virus (HIV) infections, enabling HIV incidence estimation.
    • Accurate estimation requires defining parameters like the mean window period, sensitivity, and specificity.

    Purpose of the Study:

    • To develop and validate statistical methods for estimating HIV incidence using antibody biomarker data.
    • To model biomarker dynamics post-seroconversion and derive a robust incidence estimation formula.

    Main Methods:

    • Utilized generalized mixture models to analyze biomarker distribution over time since infection.
    • Developed two methods: one using cohort data, the other using seroconverter data.
    • Applied methods to repeated measures of the IgG capture BED enzyme immunoassay.

    Main Results:

    • Both developed statistical methods yielded comparable estimates for calibration parameters (window period, recency period, sensitivity, specificity).
    • The derived incidence estimation formula provides a maximum likelihood estimate dependent on the window period, sensitivity, and specificity.
    • Numerical simulations confirmed that seroconverter data can yield reliable calibration parameter estimates.

    Conclusions:

    • The novel statistical methods offer accurate HIV incidence estimation from cross-sectional data.
    • The study provides a framework for optimizing the choice of window period in biomarker-based incidence assays.
    • Findings support the use of seroconverter data for calibrating HIV incidence assays.