用于根据患病率数据估计发病率的分段多项式。
Segmented polynomials for incidence rate estimation from prevalence data.
作者信息
Mahiané Severin Guy, Laeyendecker Oliver
机构信息
Avenir Health, 06033, Glastonbury, CT, U.S.A.
Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, 21205, MD, BaltimoreU.S.A.
出版信息
Stat Med. 2017 Jan 30;36(2):334-344. doi: 10.1002/sim.7130. Epub 2016 Sep 26.
Abstract
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.
摘要
本研究探讨了利用来自一次（或多次）横断面患病率调查的数据，估计具有可能不同死亡率的不可缓解感染（或疾病）发病率的问题。建议采用拟合分段多项式模型，使用最大似然法将发病率估计为年龄的函数。该方法允许自动搜索节点的最佳位置，并使用赤池信息准则进行模型选择。该方法应用于模拟数据，并利用美国国立精神卫生研究所项目接受（HPTN 043）和津巴布韦人口与健康调查（2005 - 2006年）的数据来估计津巴布韦男性中的艾滋病毒发病率。版权所有© 2016约翰威立父子有限公司。
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