Segmented polynomials for incidence rate estimation from prevalence data.
The study considers the problem of estimating incidence of a non remissible infection (or disease) with possibly differential mortality using data from a(several) cross-sectional prevalence survey(s). Fitting segmented polynomial models is proposed to estimate the incidence as a function of age, using the maximum likelihood method. The approach allows automatic search for optimal position of knots, and model selection is performed using the Akaike information criterion. The method is applied to simulated data and to estimate HIV incidence among men in Zimbabwe using data from both the NIMH Project Accept (HPTN 043) and Zimbabwe Demographic Health Surveys (2005-2006). Copyright © 2016 John Wiley & Sons, Ltd.
Avenir Health, 06033, Glastonbury, CT, U.S.A.|Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, 21205, MD, BaltimoreU.S.A.
Severin Guy
SG
Mahiané
Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institute of Health, Bethesda, MD, U.S.A.|Division of Infectious Diseases, The Johns Hopkins University School of Medicine, MD 21205, Baltimore, U.S.A.
Oliver
O
Laeyendecker
D000293:Adolescent; D000328:Adult; D056808:Biostatistics; D003198:Computer Simulation; D016001:Confidence Intervals; D003430:Cross-Sectional Studies; D015658:HIV Infections / Q000453:epidemiology; D006801:Humans; D015994:Incidence; D016013:Likelihood Functions; D008297:Male; D015233:Models, Statistical; D015995:Prevalence; D055815:Young Adult; D015030:Zimbabwe / Q000453:epidemiology
incidence rate; maximum likelihood estimation; model selection; mortality; prevalence; segmented polynomials
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