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.
Funding statement: 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).
Acknowledgements
The authors thank the following investigators for providing or generating data analyzed in this study: Caroline E. Mullis, Matthew M. Cousins, Thomas C. Quinn, Deborah Donnell, Connie Celum, Susan P. Buchbinder, George R. Seage III, Lisa P. Jacobson, Joseph B. Margolick, Joelle Brown, Gregory D. Kirk, Shruti H. Mehta, Richard D. Moore, and Jeanne C. Keruly. The authors gratefully acknowledge the data provided by: The HIVNET 001 Study funded by the HIV Network for Prevention Trials (HIVNET) and sponsored by the NIAID; the ALIVE Study funded by the NIDA; the MACS Study funded by the NIAID with additional supplemental funding from the National Cancer Institute (NCI) and National Heart, Lung, and Blood Institute (NHLBI); the Johns Hopkins HIV Clinical Practice Cohort was funded by NIDA, the National Institute of Alcohol Abuse and Alcoholism (NIAAA) and NIAID.
References
Brookmeyer, R. 2010a. “Measuring the HIV/AIDS Epidemic: Approaches and Challenges”. Epidemiologic Reviews 32 (1): 26–37.10.1093/epirev/mxq002Search in Google Scholar PubMed
Brookmeyer, R. 2010b. “On the Statistical Accuracy of Biomarker Assays for HIV Incidence”. Journal of Acquired Immune Deficiency Syndrome 54 (4): 406–414.10.1097/QAI.0b013e3181dc6d2cSearch in Google Scholar PubMed
Brookmeyer, R., J. Konikoff, O. Laeyendecker, and S.H. Eshleman. 2013. “Estimation of HIV Incidence Using Multiple Biomarkers.” American Journal of Epidemiology 177 (3): 264–272.10.1093/aje/kws436Search in Google Scholar PubMed PubMed Central
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): 166–172.10.1093/oxfordjournals.aje.a117404Search in Google Scholar PubMed
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): 2763–2771.10.1097/QAD.0b013e32833f1142Search in Google Scholar PubMed
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): 223–231.10.1093/infdis/jis658Search in Google Scholar PubMed PubMed Central
Kaplan, E.H., and R. Brookmeyer. 1999. “Snapshot Estimators of Recent HIV Incidence Rates”. Operations Research 47 (1): 29–37.10.1287/opre.47.1.29Search in Google Scholar
Kassanjee, R., T.A. McWalter, T. Bärnighausen, and A. Welte. 2012. “A New General Biomarker-Based Incidence Estimator.” Epidemiology 23 (5): 721–728.10.1097/EDE.0b013e3182576c07Search in Google Scholar PubMed PubMed Central
Konikoff, J. 2015. Cross-Sectional HIV Incidence Estimation: Techniques and Challenges, Los Angeles, CA: Ph.D. Dissertation. University of California at Los AngelesSearch in Google 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): 1–9.10.1371/journal.pone.0082772Search in Google Scholar PubMed PubMed Central
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): 232–239.10.1093/infdis/jis659Search in Google Scholar PubMed PubMed Central
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): 1227–1232.10.1097/QAD.0000000000000221Search in Google Scholar PubMed PubMed Central
Mastro, T.D. 2013. “Determining HIV Incidence in Populations: Moving in the Right Direction”. Journal of Infectious Diseases 207 (2): 204–206.10.1093/infdis/jis661Search in Google Scholar PubMed
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: 7.10.1371/journal.pone.0133255Search in Google Scholar PubMed PubMed Central
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): 325–327.10.1089/aid.2016.0105Search in Google Scholar PubMed PubMed Central
© 2018 Walter de Gruyter GmbH, Berlin/Boston
