Probabilistic HIV recency classification-a logistic regression without labeled individual level training data.

IF 1.3 4区 数学 Q2 STATISTICS & PROBABILITY Annals of Applied Statistics Pub Date : 2023-03-01 Epub Date: 2023-01-24 DOI:10.1214/22-aoas1618
Ben Sheng, Changcheng Li, Le Bao, Runze Li
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引用次数: 1

Abstract

Accurate HIV incidence estimation based on individual recent infection status (recent vs long-term infection) is important for monitoring the epidemic, targeting interventions to those at greatest risk of new infection, and evaluating existing programs of prevention and treatment. Starting from 2015, the Population-based HIV Impact Assessment (PHIA) individual-level surveys are implemented in the most-affected countries in sub-Saharan Africa. PHIA is a nationally-representative HIV-focused survey that combines household visits with key questions and cutting-edge technologies such as biomarker tests for HIV antibody and HIV viral load which offer the unique opportunity of distinguishing between recent infection and long-term infection, and providing relevant HIV information by age, gender, and location. In this article, we propose a semi-supervised logistic regression model for estimating individual level HIV recency status. It incorporates information from multiple data sources - the PHIA survey where the true HIV recency status is unknown, and the cohort studies provided in the literature where the relationship between HIV recency status and the covariates are presented in the form of a contingency table. It also utilizes the national level HIV incidence estimates from the epidemiology model. Applying the proposed model to Malawi PHIA data, we demonstrate that our approach is more accurate for the individual level estimation and more appropriate for estimating HIV recency rates at aggregated levels than the current practice - the binary classification tree (BCT).

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概率HIV近期分类——一种没有标记个人水平训练数据的逻辑回归。
根据个人近期感染状况(近期感染与长期感染)准确估计艾滋病毒发病率,对于监测疫情、针对新感染风险最大的人群进行干预以及评估现有的预防和治疗方案至关重要。从2015年开始,在撒哈拉以南非洲受影响最严重的国家实施基于人口的艾滋病毒影响评估(PHIA)个人层面调查。PHIA是一项具有全国代表性的以艾滋病毒为重点的调查,它将家访与关键问题和尖端技术相结合,如艾滋病毒抗体和艾滋病毒病毒载量的生物标志物测试,为区分近期感染和长期感染提供了独特的机会,并按年龄、性别和地点提供了相关的艾滋病毒信息。在这篇文章中,我们提出了一个半监督逻辑回归模型来估计个体水平的HIV近期状况。它结合了来自多个数据来源的信息——PHIA调查,其中真实的HIV近期状况未知,以及文献中提供的队列研究,其中HIV近期状况和协变量之间的关系以列联表的形式呈现。它还利用了流行病学模型中对国家一级艾滋病毒发病率的估计。将所提出的模型应用于马拉维PHIA数据,我们证明,与当前的实践——二叉分类树(BCT)相比,我们的方法更准确地用于个体水平的估计,也更适合于在总体水平上估计艾滋病毒感染率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Annals of Applied Statistics
Annals of Applied Statistics 社会科学-统计学与概率论
CiteScore
3.10
自引率
5.60%
发文量
131
审稿时长
6-12 weeks
期刊介绍: Statistical research spans an enormous range from direct subject-matter collaborations to pure mathematical theory. The Annals of Applied Statistics, the newest journal from the IMS, is aimed at papers in the applied half of this range. Published quarterly in both print and electronic form, our goal is to provide a timely and unified forum for all areas of applied statistics.
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