Spatial Small Area Smoothing Models for Handling Survey Data with Nonresponse
Document Type
Article
Publication Date
2017
Keywords
complex survey design, disease mapping, hierarchical Bayesian modelling, integrated nested Laplace approximation, missing data
Digital Object Identifier (DOI)
https://doi.org/10.1002/sim.7369
Abstract
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.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Statistics in Medicine, v. 36, issue 23, p. 3708-3745
Scholar Commons Citation
Watjou, K.; Faes, C.; Lawson, A.; Kirby, R. S.; Aregay, M.; Carroll, R.; and Vandendijck, Y., "Spatial Small Area Smoothing Models for Handling Survey Data with Nonresponse" (2017). Community and Family Health Faculty Publications. 58.
https://digitalcommons.usf.edu/cfh_facpub/58
