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Segmented polynomials for incidence rate estimation from prevalence data
Author(s) -
Mahiané Severin Guy,
Laeyendecker Oliver
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7130
Subject(s) - akaike information criterion , incidence (geometry) , statistics , estimation , maximum likelihood , selection (genetic algorithm) , polynomial , demography , mathematics , medicine , computer science , artificial intelligence , mathematical analysis , geometry , management , sociology , economics
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.