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Statistical Communications in Infectious Diseases

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Mathematical Citation Quotient (MCQ) 2016: 0.06

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1948-4690
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Cross-Sectional HIV Incidence Surveillance: A Benchmarking of Approaches for Estimating the ‘Mean Duration of Recent Infection’

Reshma Kassanjee
  • Corresponding author
  • Department of Statistical Sciences, University of Cape Town, Rondebosch 7701, South Africa
  • Stellenbosch University, The South African DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch, South Africa
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/ Daniela De Angelis
  • Medical Research Council, MRC Biostatistics Unit, Cambridge, United Kingdom of Great Britain and Northern Ireland
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/ Marian Farah
  • Medical Research Council, MRC Biostatistics Unit, Cambridge, United Kingdom of Great Britain and Northern Ireland
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/ Debra Hanson / Jan Phillipus Lourens Labuschagne
  • Stellenbosch University, The South African DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch, South Africa
  • South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa
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/ Oliver Laeyendecker
  • Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA
  • Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
  • Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
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/ Stéphane Le Vu
  • Institut National de la Santé et de la Recherche Médicale – U1018, Centre de Recherche en Épidémiologie et Santé des Populations, Université Paris Sud, Le Kremlin Bicêtre, France
  • Département des Maladies Infectieuses, Institut de Veille Sanitaire, Saint-Maurice, France
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/ Brian Tom
  • Medical Research Council, MRC Biostatistics Unit, Cambridge, United Kingdom of Great Britain and Northern Ireland
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/ Rui Wang
  • Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA USA
  • Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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/ Alex Welte
  • Stellenbosch University, The South African DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch, South Africa
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Published Online: 2017-03-14 | DOI: https://doi.org/10.1515/scid-2016-0002

Abstract

The application of biomarkers for ‘recent’ infection in cross-sectional HIV incidence surveillance requires the estimation of critical biomarker characteristics. Various approaches have been employed for using longitudinal data to estimate the Mean Duration of Recent Infection (MDRI) – the average time in the ‘recent’ state. In this systematic benchmarking of MDRI estimation approaches, a simulation platform was used to measure accuracy and precision of over twenty approaches, in thirty scenarios capturing various study designs, subject behaviors and test dynamics that may be encountered in practice. Results highlight that assuming a single continuous sojourn in the ‘recent’ state can produce substantial bias. Simple interpolation provides useful MDRI estimates provided subjects are tested at regular intervals. Regression performs the best – while ‘random effects’ describe the subject-clustering in the data, regression models without random effects proved easy to implement, stable, and of similar accuracy in scenarios considered; robustness to parametric assumptions was improved by regressing ‘recent’/‘non-recent’ classifications rather than continuous biomarker readings. All approaches were vulnerable to incorrect assumptions about subjects’ (unobserved) infection times. Results provided show the relationships between MDRI estimation performance and the number of subjects, inter-visit intervals, missed visits, loss to follow-up, and aspects of biomarker signal and noise.

This article offers supplementary material which is provided at the end of the article.

Keywords: HIV; incidence estimation; duration of recent infection; cross-sectional incidence surveys; biomarkers for recent infection

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About the article

Received: 2016-05-16

Accepted: 2016-11-14

Revised: 2016-09-21

Published Online: 2017-03-14


South African National Research Foundation, (Grant /Award Number: ‘UID 44895’); Bill and Melinda Gates Foundation, (Grant /Award Number: ‘OPP1022972’); Medical Research Council, (Grant /Award Number: ‘Unit Programme number MC_UP_1302/3’, ‘Unit Programme number U105260566’); National Institutes of Health, (Grant /Award Number: ‘R01 AI095068’, ‘R37 AI51164’); Division of Intramural Research, National Institute of Allergy and Infectious Diseases.


Citation Information: Statistical Communications in Infectious Diseases, ISSN (Online) 1948-4690, DOI: https://doi.org/10.1515/scid-2016-0002.

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