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Augmented Cross-Sectional Studies with Abbreviated Follow-up for Estimating HIV Incidence

Authors

  • B. Claggett,

    1. Department of Biostatistics, Harvard University School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115, U.S.A.
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  • S. W. Lagakos,

    1. Department of Biostatistics, Harvard University School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115, U.S.A.
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  • R. Wang

    Corresponding author
    1. Department of Biostatistics, Harvard University School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115, U.S.A.
      email: rwang@hsph.harvard.edu
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email: rwang@hsph.harvard.edu

Abstract

Summary Cross-sectional HIV incidence estimation based on a sensitive and less-sensitive test offers great advantages over the traditional cohort study. However, its use has been limited due to concerns about the false negative rate of the less-sensitive test, reflecting the phenomenon that some subjects may remain negative permanently on the less-sensitive test. Wang and Lagakos (2010, Biometrics66, 864–874) propose an augmented cross-sectional design that provides one way to estimate the size of the infected population who remain negative permanently and subsequently incorporate this information in the cross-sectional incidence estimator. In an augmented cross-sectional study, subjects who test negative on the less-sensitive test in the cross-sectional survey are followed forward for transition into the nonrecent state, at which time they would test positive on the less-sensitive test. However, considerable uncertainty exists regarding the appropriate length of follow-up and the size of the infected population who remain nonreactive permanently to the less-sensitive test. In this article, we assess the impact of varying follow-up time on the resulting incidence estimators from an augmented cross-sectional study, evaluate the robustness of cross-sectional estimators to assumptions about the existence and the size of the subpopulation who will remain negative permanently, and propose a new estimator based on abbreviated follow-up time (AF). Compared to the original estimator from an augmented cross-sectional study, the AF estimator allows shorter follow-up time and does not require estimation of the mean window period, defined as the average time between detectability of HIV infection with the sensitive and less-sensitive tests. It is shown to perform well in a wide range of settings. We discuss when the AF estimator would be expected to perform well and offer design considerations for an augmented cross-sectional study with abbreviated follow-up.

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