论文标题
小面积的健康结果估计
Small Area Estimation of Health Outcomes
论文作者
论文摘要
小面积估计（SAE）需要估计域（通常是地理区域）感兴趣的特征，其中可能几乎没有样品。 SAE从一系列令人困惑的哲学角度来看，已经提出了各种各样的历史，并提出了多种方法。我们描述了在区域级别和单位级别上指定的基于设计和模型的方法和模型，重点是健康应用和完全贝叶斯的空间模型。当响应数据稀疏时，辅助信息的使用是成功推断的关键要素，我们讨论了允许包含协变量数据的许多方法。使用2015 - 2016年马拉维人口健康调查收集的数据，用于艾滋病毒患病率的SAE用于说明许多技术。讨论了SAE技术在与Covid-19相关的结果中的潜在用途。
Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area-level and at the unit-level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015-2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to COVID-19 is discussed.
