Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23986
Title: Multiscale measurement error models for aggregated small area health data
Authors: Aregay, Mehreteab
Lawson, Andrew B.
Faes, Christel 
Kirby, Russell S.
Carroll, Rachel
Watjou, Kevin 
Issue Date: 2016
Source: STATISTICAL METHODS IN MEDICAL RESEARCH, 25(4), p. 1201-1223
Abstract: Spatial data are often aggregated from a finer (smaller) to a coarser (larger) geographical level. The process of data aggregation induces a scaling effect which smoothes the variation in the data. To address the scaling problem, multiscale models that link the convolution models at different scale levels via the shared random effect have been proposed. One of the main goals in aggregated health data is to investigate the relationship between predictors and an outcome at different geographical levels. In this paper, we extend multiscale models to examine whether a predictor effect at a finer level hold true at a coarser level. To adjust for predictor uncertainty due to aggregation, we applied measurement error models in the framework of multiscale approach. To assess the benefit of using multiscale measurement error models, we compare the performance of multiscale models with and without measurement error in both real and simulated data. We found that ignoring the measurement error in multiscale models underestimates the regression coefficient, while it overestimates the variance of the spatially structured random effect. On the other hand, accounting for the measurement error in multiscale models provides a better model fit and unbiased parameter estimates.
Keywords: measurement error; multiscale models; scaling effect; shared random effects; convolution models
Document URI: http://hdl.handle.net/1942/23986
ISSN: 0962-2802
e-ISSN: 1477-0334
DOI: 10.1177/0962280216661094
ISI #: 000382871200008
Rights: (C) The Author(s) 2016
Category: A1
Type: Journal Contribution
Validations: ecoom 2017
Appears in Collections:Research publications

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