Cross-Sectional HIV Incidence Estimation with Missing Biomarkers.

Doug Morrison, Oliver Laeyendecker, Jacob Konikoff, Ron Brookmeyer

Abstract read

In one paragraph

Article in Statistical communications in infectious diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Doug MorrisonDepartment of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.

Oliver LaeyendeckerLaboratory of Immunoregulation, NIAID, NIH, Baltimore, MD, USA and The Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.

Jacob KonikoffConsultant; work completed as postdoctoral fellow, Johns Hopkins University, Baltimore, Maryland, USA.

Ron BrookmeyerDepartment of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.

Funding

International Studies of the Acquired Immune Deficiency Syndrome (AIDS)ZIAAI000361 · NIAID · NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES · PI REDD, ANDREW · 2009 to 2025

$15.8M

Laboratory and Statistical Development of Cross-Sectional HIV Incidence AssaysR01AI095068 · NIAID · JOHNS HOPKINS UNIVERSITY · PI ESHLEMAN, SUSAN H · 2011 to 2021

$6.6M

NIAID NIH HHS R01 AI095068

6 · The paper itself

Abstract

Considerable progress has been made in the development of approaches for HIV incidence estimation based on a cross-sectional survey for biomarkers of recent infection. Multiple biomarkers when used in combination can increase the precision of cross-sectional HIV incidence estimates. Multi-assay algorithms (MAAs) for cross-sectional HIV incidence estimation are hierarchical stepwise algorithms for testing the biological samples with multiple biomarkers. The objective of this paper is to consider some of the statistical challenges for addressing the problem of missing biomarkers in such testing algorithms. We consider several methods for handling missing biomarkers for (1) estimating the mean window period, and (2) estimating HIV incidence from a cross sectional survey once the mean window period has been determined. We develop a conditional estimation approach for addressing the missing data challenges and compare that method with two naïve approaches. Using MAAs developed for HIV subtype B, we evaluate the methods by simulation. We show that the two naïve estimation methods lead to biased results in most of the missing data scenarios considered. The proposed conditional approach protects against bias in all of the scenarios.

Indexed as

biomarkerscross-sectional studiesHIVincidencemissing data

Identifiers

PMID30701015

DOI10.1515/scid-2017-0003

PMCPMC6349386

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.