Bootstrap confidence intervals and bias correction in the estimation of HIV incidence from surveillance data with testing for recent infection

Statistics in Medicine

Volumn 30, Issue 8, 2011, Pages 854-865

  Carnegie, Nicole Bohme ^a  

^a NEW YORK UNIVERSITY   (United States)

Author keywords

HIV incidence; Model based bootstrap; Testing for recent infection

Indexed keywords

ACCURACY; ANALYSIS OF VARIANCE; ARTICLE; BOOTSTRAPPING; COMPUTER PROGRAM; CONFIDENCE INTERVAL; DISEASE SURVEILLANCE; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; MATHEMATICAL COMPUTING; STATISTICAL MODEL; SYSTEMATIC ERROR;

ALGORITHMS; ANALYSIS OF VARIANCE; BIAS (EPIDEMIOLOGY); BIOSTATISTICS; CENTERS FOR DISEASE CONTROL AND PREVENTION (U.S.); COHORT STUDIES; CONFIDENCE INTERVALS; EPIDEMICS; HIV INFECTIONS; HUMANS; INCIDENCE; MODELS, STATISTICAL; POPULATION SURVEILLANCE; UNITED STATES; WASHINGTON;

EID: 79952998414     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4134     Document Type: Article

References (20)

1. The UNAIDS Reference Group on Estimates Modelling & Projections. Improved methods and assumptions for estimation of the HIV/AIDS epidemic and its impact: recommendations of the UNAIDS Reference Group on Estimates, Modelling and Projections. AIDS 2002; 16:W1-W14.
2. Janssen RS, Satten GA, Stramer SL, Rawal BD, O'Brien TR, Weiblen BJ, Hecht FM, Jack N, Cleghorn FR, Kahn JO, Chesney MA, Busch MP. New testing strategy to detect early HIV-1 infection for use in incidence estimates and for clinical and prevention purposes. Journal of the American Medical Association 1998; 280(1):42-48. DOI: 10.1001/jama.280.1.42.
3. Parekh BS, Susan Kennedy M, Dobbs T, Pau C-P, Byers R, Green T, Hu DJ, Vanichseni S, Young NL, Choopanya K, Mastro TD, Steven McDougal J. Quantitative detection of increasing HIV type 1 antibodies after seroconversion: a simple assay for detecting recent HIV infection and estimating incidence. AIDS Research and Human Retroviruses 2002; 18(4):295-307. DOI: 10.1089/088922202753472874.
4. Cole SR, Chu H, Brookmeyer R. Confidence intervals for biomarker-based human immunodeficiency virus incidence estimates and differences using prevalent data. American Journal of Epidemiology 2006; 165(1):94-100. DOI: 10.1093/aje/kwj322.
5. Karon JM, Song R, Brookmeyer R, Kaplan EH, Hall HI. Estimating HIV incidence in the United States from HIV/AIDS surveillance data and biomarker HIV test results. Statistics in Medicine 2008; 27:4617-4633. DOI: 10.1002/sim.3144.
6. Irene Hall H, Song R, Rhodes P, Prejean J, An Q, Lee LM, Karon J, Brookmeyer R, Kaplan EH, McKenna MT, Janssen RS, HIV Incidence Surveillance Group. Estimation of HIV incidence in the United States. Journal of the American Medical Association 2008; 300(5):520-529. DOI: 10.1001/jama.300.5.520.
7. Casella G, Berger RL. Statistical Inference. Duxbury Press: Belmont, CA, 2002.
8. Shao J, Tu D. The Jackknife and Bootstrap. Springer: New York, 1995.
9. Wasserman L. All of Nonparametric Statistics. Springer: New York, 2006.
10. Efron B, Tibshirani RJ. An Introduction to the Bootstrap. Monographs on Statistics and Applied Probability, vol. 57. Chapman & Hall: New York, 1993.
11. R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria, 2008; ISBN: 3-900051-07-0. Available from:.
12. Centers for Disease Control & Prevention. Revised surveillance case definitions for HIV infection among adults, adolescents, and children aged <18 months and for HIV infection and AIDS among children aged 18 months to <13 years-United States, 2008. Morbidity and Mortality Weekly Report 2008; 57(RR-10):1-8.
13. Crum-Cianflone N, Eberly L, Zhang Y, Ganersan A, Weintrob A, Marconi V, Barthel RV, Fraser S, Agan BK, Wegner S. Is HIV becoming more virulent? Initial CD4 cell counts among HIV seroconverters during the course of the HIV epidemic: 1985-2007. Clinical Infectious Diseases 2009; 48:1285-1292. DOI: 10.1086/597777.
14. McDougal JS, Parekh BS, Peterson ML, Branson BM, Dobbs T, Ackers M, Gurwith M. Comparison of HIV type 1 incidence observed during longitudinal follow-up with incidence estimated by cross-sectional analysis using the BED capture enzyme immunoassay. AIDS Research and Human Retroviruses 2006; 22(10):945-952. DOI: 10.1089/aid.2006.22.945.
15. Becker NG, Chao X. Dependent HIV incidences in back-projection of AIDS incidence data. Statistics in Medicine 1994; 13:1945-1958. DOI: 10.1002/sim.4780131907.
16. Bellocco R, Pagano M. Multinomial analysis of smoothed HIV back-calculation models incorporating uncertainty in the AIDS incidence. Statistics in Medicine 2001; 20:2017-2033. DOI: 10.1002/sim.818.
17. Chau P, Yip PS, Cui JS. Reconstructing the incidence of human immunodeficiency virus (HIV) in Hong Kong by using data from HIV positive tests and diagnoses of acquired immune deficiency syndrome. Applied Statistics 2003; 52(2):237-248. DOI: 10.1111/1467-9876.00401.
18. Gray RH, Wawer MJ, Brookmeyer R, Sewankambo NK, Serwadda D, Wabwire-Mangen F, Lutalo T, Li X, vanCott T, Quinn TC, The Rakai Project Team. Probability of HIV-1 transmission per coital act in monogamous, heterosexual, HIV-1-discordant couples in Rakai, Uganda. Lancet 2001; 357(9263):1149-1153.
19. White EW, Lumley T, Goodreau SM, Goldbaum GM, Hawes SE. Stochastic models to demonstrate the effect of motivated testing on HIV incidence estimates using the serologic testing algorithm for recent HIV seroconversion (STARHS). Sexually Transmitted Infections, in press.
20. Hargrove JW, Humphrey JH, Mutasa K, Parekh BS, Steve McDougal J, Ntozini R, Chidawanyika H, Moulton LH, Ward B, Nathoo K, Iliff PJ, Kopp E. Improved HIV-1 incidence estimates using the BED capture enzyme immunoassay. AIDS 2008; 22(4):511-518. DOI: 10.1097/QAD.0b013e3282f2a960.