Smooth individual level covariates adjustment in disease mapping

Huque, M. H., Anderson, C. , Walton, R., Woolford, S. and Ryan, L. (2018) Smooth individual level covariates adjustment in disease mapping. Biometrical Journal, 60(3), pp. 597-615. (doi:10.1002/bimj.201700143) (PMID:29577405)

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Abstract

Spatial models for disease mapping should ideally account for covariates measured both at individual and area levels. The newly available “indiCAR” model fits the popular conditional autoregresssive (CAR) model by accommodating both individual and group level covariates while adjusting for spatial correlation in the disease rates. This algorithm has been shown to be effective but assumes log‐linear associations between individual level covariates and outcome. In many studies, the relationship between individual level covariates and the outcome may be non‐log‐linear, and methods to track such nonlinearity between individual level covariate and outcome in spatial regression modeling are not well developed. In this paper, we propose a new algorithm, smooth‐indiCAR, to fit an extension to the popular conditional autoregresssive model that can accommodate both linear and nonlinear individual level covariate effects while adjusting for group level covariates and spatial correlation in the disease rates. In this formulation, the effect of a continuous individual level covariate is accommodated via penalized splines. We describe a two‐step estimation procedure to obtain reliable estimates of individual and group level covariate effects where both individual and group level covariate effects are estimated separately. This distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. We evaluate the performance of smooth‐indiCAR through simulation. Our results indicate that the smooth‐indiCAR method provides reliable estimates of all regression and random effect parameters. We illustrate our proposed methodology with an analysis of data on neutropenia admissions in New South Wales (NSW), Australia.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Anderson, Dr Craig
Authors: Huque, M. H., Anderson, C., Walton, R., Woolford, S., and Ryan, L.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Biometrical Journal
Publisher:Wiley
ISSN:0323-3847
ISSN (Online):1521-4036
Published Online:25 March 2018
Copyright Holders:Copyright © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
First Published:First published in Biometrical Journal 60(3):597-615
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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