Rosin Samuel P, Shook-Sa Bonnie E, Cole Stephen R, Hudgens Michael G
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
J R Stat Soc Ser A Stat Soc. 2023 May 19;186(4):834-851. doi: 10.1093/jrsssa/qnad068. eCollection 2023 Oct.
Governments and public health authorities use seroprevalence studies to guide responses to the COVID-19 pandemic. Seroprevalence surveys estimate the proportion of individuals who have detectable SARS-CoV-2 antibodies. However, serologic assays are prone to misclassification error, and non-probability sampling may induce selection bias. In this paper, non-parametric and parametric seroprevalence estimators are considered that address both challenges by leveraging validation data and assuming equal probabilities of sample inclusion within covariate-defined strata. Both estimators are shown to be consistent and asymptotically normal, and consistent variance estimators are derived. Simulation studies are presented comparing the estimators over a range of scenarios. The methods are used to estimate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence in New York City, Belgium, and North Carolina.
政府和公共卫生当局利用血清流行率研究来指导应对新冠疫情。血清流行率调查估计具有可检测到的严重急性呼吸综合征冠状病毒2（SARS-CoV-2）抗体的个体比例。然而，血清学检测容易出现错误分类误差，非概率抽样可能会导致选择偏差。在本文中，考虑了非参数和参数血清流行率估计方法，这些方法通过利用验证数据并假设在协变量定义的分层中样本纳入的概率相等来应对这两个挑战。两种估计方法均显示出一致性和渐近正态性，并推导了一致的方差估计量。给出了模拟研究，比较了一系列场景下的估计方法。这些方法被用于估计纽约市、比利时和北卡罗来纳州的SARS-CoV-2血清流行率。
