TY - JOUR
T1 - Cross-sectional human immunodeficiency virus incidence estimation accounting for heterogeneity across communities
AU - Xu, Yuejia
AU - Laeyendecker, Oliver
AU - Wang, Rui
N1 - Funding Information:
We thank the coeditor, associate editor, and two referees for their insightful comments and suggestions, which led to an improved paper. This research was supported by grants R01 AI136947, R37 AI51164, U01/UM1 AI068613, U01/UM1 AI068617, U01/UM1 AI068619 and R01 AI095068 from the US National Institute of Allergy and Infectious Diseases (NIAID). In addition, this work was supported by HPTN Protocol 043 through contracts U01AI068613 and UM1A068613 (HPTN Network Laboratory, Eshleman S, PI); U01AI068617/UM1AI068617 (SCHARP, Donnell D, PI); U01AI068619/UM1AI068619 (HIV Prevention Trials Network, Vermund S/El‐Sadr W, PIs); NIAIDR01 AI095068 (Brookmeyer R/Eshleman S, PIs); and by the Office of AIDS Research, and the Division of Intramural Research, NIAID.
Publisher Copyright:
© 2019 The International Biometric Society
PY - 2019/9/1
Y1 - 2019/9/1
N2 - Accurate estimation of human immunodeficiency virus (HIV) incidence rates is crucial for the monitoring of HIV epidemics, the evaluation of prevention programs, and the design of prevention studies. Traditional cohort approaches to measure HIV incidence require repeatedly testing large cohorts of HIV-uninfected individuals with an HIV diagnostic test (eg, enzyme-linked immunosorbent assay) for long periods of time to identify new infections, which can be prohibitively costly, time-consuming, and subject to loss to follow-up. Cross-sectional approaches based on the usual HIV diagnostic test and biomarkers of recent infection offer important advantages over standard cohort approaches, in terms of time, cost, and attrition. Cross-sectional samples usually consist of individuals from different communities. However, small sample sizes limit the ability to estimate community-specific incidence and existing methods typically ignore heterogeneity in incidence across communities. We propose a permutation test for the null hypothesis of no heterogeneity in incidence rates across communities, develop a random-effects model to account for this heterogeneity and to estimate community-specific incidence, and provide one way to estimate the coefficient of variation. We evaluate the performance of the proposed methods through simulation studies and apply them to the data from the National Institute of Mental Health Project ACCEPT, a phase 3 randomized controlled HIV prevention trial in Sub-Saharan Africa, to estimate the overall and community-specific HIV incidence rates.
AB - Accurate estimation of human immunodeficiency virus (HIV) incidence rates is crucial for the monitoring of HIV epidemics, the evaluation of prevention programs, and the design of prevention studies. Traditional cohort approaches to measure HIV incidence require repeatedly testing large cohorts of HIV-uninfected individuals with an HIV diagnostic test (eg, enzyme-linked immunosorbent assay) for long periods of time to identify new infections, which can be prohibitively costly, time-consuming, and subject to loss to follow-up. Cross-sectional approaches based on the usual HIV diagnostic test and biomarkers of recent infection offer important advantages over standard cohort approaches, in terms of time, cost, and attrition. Cross-sectional samples usually consist of individuals from different communities. However, small sample sizes limit the ability to estimate community-specific incidence and existing methods typically ignore heterogeneity in incidence across communities. We propose a permutation test for the null hypothesis of no heterogeneity in incidence rates across communities, develop a random-effects model to account for this heterogeneity and to estimate community-specific incidence, and provide one way to estimate the coefficient of variation. We evaluate the performance of the proposed methods through simulation studies and apply them to the data from the National Institute of Mental Health Project ACCEPT, a phase 3 randomized controlled HIV prevention trial in Sub-Saharan Africa, to estimate the overall and community-specific HIV incidence rates.
KW - biomarkers
KW - coefficient of variation
KW - permutation test
KW - random-effects model
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U2 - 10.1111/biom.13046
DO - 10.1111/biom.13046
M3 - Article
C2 - 30746695
AN - SCOPUS:85072057621
VL - 75
SP - 1017
EP - 1028
JO - Biometrics
JF - Biometrics
SN - 0006-341X
IS - 3
ER -