Watjou K, Faes C, Lawson A, Kirby R S, Aregay M, Carroll R, Vandendijck Y
Interuniversity Institute for Statistics and Statistical Bioinformatics, Hasselt University, 3590, Hasselt, Belgium.
Department of Public Health Sciences, Medical University of South Carolina, 135 Cannon St, Charleston, SC 29425, USA.
Stat Med. 2017 Oct 15;36(23):3708-3745. doi: 10.1002/sim.7369. Epub 2017 Jul 2.
Spatial smoothing models play an important role in the field of small area estimation. In the context of complex survey designs, the use of design weights is indispensable in the estimation process. Recently, efforts have been made in these spatial smoothing models, in order to obtain reliable estimates of the spatial trend. However, the concept of missing data remains a prevalent problem in the context of spatial trend estimation as estimates are potentially subject to bias. In this paper, we focus on spatial health surveys where the available information consists of a binary response and its associated design weight. Furthermore, we investigate the impact of nonresponse as missing data on a range of spatial models for different missingness mechanisms and different degrees of missingness by means of an extensive simulation study. The computations were performed in R, using INLA and other existing packages. The results show that weight adjustment to correct for missingness has a beneficial effect on the bias in the missing at random setting for all models. Furthermore, we estimate the geographical distribution of perceived health at the district level based on the Belgian Health Interview Survey (2001). Copyright © 2017 John Wiley & Sons, Ltd.
空间平滑模型在小区域估计领域发挥着重要作用。在复杂的调查设计背景下，设计权重的使用在估计过程中不可或缺。最近，人们在这些空间平滑模型方面做出了努力，以便获得空间趋势的可靠估计。然而，在空间趋势估计的背景下，缺失数据的概念仍然是一个普遍存在的问题，因为估计可能会受到偏差的影响。在本文中，我们关注的是空间健康调查，其中可用信息包括二元响应及其相关的设计权重。此外，我们通过广泛的模拟研究，调查了不同缺失机制和不同缺失程度下，无应答作为缺失数据对一系列空间模型的影响。计算是在R中使用INLA和其他现有软件包进行的。结果表明，对于所有模型，在随机缺失设置下，通过权重调整来校正缺失对偏差有有益影响。此外，我们基于比利时健康访谈调查（2001年）估计了地区层面的感知健康地理分布。版权所有© 2017约翰·威利父子有限公司。
