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Statistical Communications in Infectious Diseases

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Cross-Sectional HIV Incidence Estimation with Missing Biomarkers

Doug Morrison
  • Corresponding author
  • Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA
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/ Oliver Laeyendecker
  • Laboratory of Immunoregulation, NIAID, NIH, Baltimore, MD, USA
  • The Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
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/ Jacob Konikoff / Ron Brookmeyer
  • Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA
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Published Online: 2018-07-31 | DOI: https://doi.org/10.1515/scid-2017-0003


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.

Keywords: biomarkers; cross-sectional studies; HIV; incidence; missing data


  • Brookmeyer, R. 2010a. “Measuring the HIV/AIDS Epidemic: Approaches and Challenges”. Epidemiologic Reviews 32 (1): 26–37.CrossrefWeb of ScienceGoogle Scholar

  • Brookmeyer, R. 2010b. “On the Statistical Accuracy of Biomarker Assays for HIV Incidence”. Journal of Acquired Immune Deficiency Syndrome 54 (4): 406–414.CrossrefGoogle Scholar

  • Brookmeyer, R., J. Konikoff, O. Laeyendecker, and S.H. Eshleman. 2013. “Estimation of HIV Incidence Using Multiple Biomarkers.” American Journal of Epidemiology 177 (3): 264–272.CrossrefWeb of SciencePubMedGoogle Scholar

  • Brookmeyer, R., and T. Quinn. 1995. “Estimation of Current Human Immunodeficiency Virus Incidence Rates from a Cross-Sectional Survey Using Early Diagnostic Tests”. American Journal of Epidemiology 141 (2): 166–172.CrossrefPubMedGoogle Scholar

  • Busch, M.P., C.D. Pilcher, T.D. Mastro, J. Kaldor, G. Vercauteren, W. Rodriguez, et al. 2010. “Beyond Detuning: 10 Years of Progress and New Challenges in the Development and Application of Assays for HIV Incidence Estimation.” AIDS 24 (18): 2763–2771.CrossrefWeb of SciencePubMedGoogle Scholar

  • Eshleman, S.H., J.P. Hughes, O. Laeyendecker, J. Wang, R. Brookmeyer, L. Johnson-Lewis, et al. 2013. “Use of a Multifaceted Approach to Analyze HIV Incidence in a Cohort Study of Women in the United States: HIV Prevention Trials Network 064 Study.” Journal of Infectious Diseases 207 (2): 223–231.Web of ScienceCrossrefGoogle Scholar

  • Kaplan, E.H., and R. Brookmeyer. 1999. “Snapshot Estimators of Recent HIV Incidence Rates”. Operations Research 47 (1): 29–37.CrossrefGoogle Scholar

  • Kassanjee, R., T.A. McWalter, T. Bärnighausen, and A. Welte. 2012. “A New General Biomarker-Based Incidence Estimator.” Epidemiology 23 (5): 721–728.PubMedWeb of ScienceCrossrefGoogle Scholar

  • Konikoff, J. 2015. Cross-Sectional HIV Incidence Estimation: Techniques and Challenges, Los Angeles, CA: Ph.D. Dissertation. University of California at Los AngelesGoogle Scholar

  • Konikoff, J., R. Brookmeyer, A.F. Longosz, M.M. Cousins, C. Celum, S.P. Buchbinder, et al. 2013. “Performance of a Limiting-Antigen Avidity Enzyme Immunoassay for Cross-Sectional Estimation of HIV Incidence in the United States.” PLoS ONE 8 (12): 1–9.Web of ScienceGoogle Scholar

  • Laeyendecker, O., R. Brookmeyer, M.M. Cousins, C.E. Mullis, J. Konikoff, D. Donnell, C. Celum, S.P. Buchbinder, G.R. Seage, G.D. Kirk, S.H. Mehta, J. Astemborski, L.P. Jacobson, J.B. Margolick, J. Brown, T.C. Quinn, and S.H. Eshleman. 2013. “HIV Incidence Determination in the United States: A Multiassay Approach.” Journal of Infectious Diseases 207 (2): 232–239.CrossrefWeb of ScienceGoogle Scholar

  • Longosz, A.F., S.H. Mehta, G.D. Kirk, J.B. Margolick, J. Brown, T.C. Quinn, et al. 2014. “Incorrect Identification of Recent HIV Infection in Adults in the United States Using a Limiting-Antigen Avidity Assay.” AIDS 28 (8): 1227–1232.Web of ScienceCrossrefPubMedGoogle Scholar

  • Mastro, T.D. 2013. “Determining HIV Incidence in Populations: Moving in the Right Direction”. Journal of Infectious Diseases 207 (2): 204–206.Web of ScienceCrossrefGoogle Scholar

  • Rehle, T., L. Johnson, T. Hallett, M. Mahy, A. Kim, H. Odido, et al. 2015. “A Comparison of South African National HIV Incidence Estimates: A Critical Appraisal of Different Methods.” PLoS ONE 10: 7.Web of ScienceGoogle Scholar

  • Wendel, S.K., A.F. Longosz, S.H. Eshleman, J.N. Blankson, R.D. Moore, J.C. Keruly, et al. 2017. “Short Communication: The Impact of Viral Suppression and Viral Breakthrough on Limited-Antigen Avidity Assay Results in Individuals with Clade B HIV Infection.” AIDS Research and Human Retroviruses 33 (4): 325–327.Web of SciencePubMedCrossrefGoogle Scholar

About the article

Received: 2017-12-20

Accepted: 2018-06-01

Revised: 2018-06-01

Published Online: 2018-07-31

This work was supported by R01-AI095068 (DM, JK, RB) sponsored by NIAID of the National Institutes of Health (NIH), and the Division of Intramural Research, NIAID (OL).

Citation Information: Statistical Communications in Infectious Diseases, Volume 10, Issue 1, 20170003, ISSN (Online) 1948-4690, DOI: https://doi.org/10.1515/scid-2017-0003.

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