Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/24962
Title: Spatial small area smoothing models for handling survey data with nonresponse
Authors: WATJOU, Kevin 
FAES, Christel 
LAWSON, Andrew 
Kirby, R. S.
AREGAY, Mehreteab 
Carroll, R.
VANDENDIJCK, Yannick 
Issue Date: 2017
Publisher: WILEY
Source: STATISTICS IN MEDICINE, 36(23), p. 3708-3745
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 (c) 2017 John Wiley & Sons, Ltd.
Notes: [Watjou, K.; Faes, C.; Vandendijck, Y.] Hasselt Univ, Interuniv Inst Stat & Stat Bioinformat, B-3590 Hasselt, Belgium. [Lawson, A.; Aregay, M.; Carroll, R.] Med Univ South Carolina, Dept Publ Hlth Sci, 135 Cannon St, Charleston, SC 29425 USA. [Kirby, R. S.] Univ S Florida, Dept Community & Family Hlth, Tampa, FL 33620 USA.
Keywords: complex survey design; disease mapping; hierarchical Bayesian modelling; integrated nested Laplace approximation; missing data;complex survey design; disease mapping; hierarchical Bayesian modelling; integrated nested Laplace approximation; missing data
Document URI: http://hdl.handle.net/1942/24962
ISSN: 0277-6715
e-ISSN: 1097-0258
DOI: 10.1002/sim.7369
ISI #: 000409236500008
Rights: © 2017 John Wiley & Sons, Ltd.
Category: A1
Type: Journal Contribution
Validations: ecoom 2018
Appears in Collections:Research publications

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