Estimating the distribution of the window period for recent HIV infections: A comparison of statistical methods

Michael J. Sweeting*, Daniela De Angelis, John Parry, Barbara Suligoi

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    21 Citations (Scopus)


    In the past few years a number of antibody biomarkers have been developed to distinguish between recent and established Human Immunodeficiency Virus (HIV) infection. Typically, a specific threshold/cut-off of the biomarker is chosen, values below which are indicative of recent infections. Such biomarkers have attracted considerable interest as the basis for incidence estimation using a cross-sectional sample. An estimate of HIV incidence can be obtained from the prevalence of recent infection, as measured in the sample, and knowledge of the time spent in the recent infection state, known as the window period. However, such calculations are based on a number of assumptions concerning the distribution of the window period. We compare two statistical methods for estimating the mean and distribution of a window period using data on repeated measurements of an antibody biomarker from a cohort of HIV seroconverters. The methods account for the interval-censored nature of both the date of seroconversion and the date of crossing a specific threshold. We illustrate the methods using repeated measurements of the Avidity Index (AI) and make recommendations about the choice of threshold for this biomarker so that the resulting window period satisfies the assumptions for incidence estimation.

    Original languageEnglish
    Pages (from-to)3194-3202
    Number of pages9
    JournalStatistics in Medicine
    Issue number30
    Publication statusPublished - 30 Dec 2010


    • Biomarker
    • HIV incidence
    • Mixed-effects models
    • Window period


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