Bayesian hierarchical modelling of noisy spatial rates on a modestly large and discontinuous irregular lattice. (December 2014)

• Record Type: Journal Article
• Authors: MacNab, Ying C; Read, Simon; Strong, Mark; Pearson, Tim; Maheswaran, Ravi; Goyder, Elizabeth
• Other Names: Lawson Andrew B guest-editor.; Ugarte María Dolores guest-editor.; MacNab Ying C guest-editor.; Lee Duncan guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 23:Number 6(2014:Dec.)
• Page Start: 552
• Page End: 571
• Publication Date: 2014-12
• Subjects: Hierarchical models -- Bernoulli-binomial spatial mixture -- zero-inflated binomial (ZIB) model -- intrinsic CAR -- BYM model -- modified BYM model -- discontinuous irregular lattice -- response rate -- public health intervention -- postcode areas; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280214527386
• Languages: English
• ISSNs: 0962-2802

Abstract

We present Bayesian hierarchical spatial model development motivated from a recent analysis of noisy small area response rate data, named the Booster data. The Booster data are postcode-level aggregates from a recent mail-out recruitment for a physical exercise intervention in deprived urban neighbourhoods in Sheffield, UK. Bayesian hierarchical Bernoulli-binomial spatial mixture zero-inflated Binomial models were developed for modelling overdispersion and for separation of systematic and random variations in the noisy and mostly low crude response rates. We present methods that enabled us to explore the underlying spatial rate variation, clustering of low or high response rate areas and neighbourhood characteristics that were associated with variations and patterns of invitation mail-outs, zero-response and response rates. Three spatial prior formulations, the intrinsic conditional autoregressive or (iCAR), the Besag-York-Mollié (BYM) and the modified BYM models, were explored for their performance on modelling sparse data on a modestly large and discontinuous irregular lattice. An in-depth Bayesian analysis of the Booster data is presented, with the resulting posterior estimation and inference implemented via Markov chain Monte Carlo simulation in WinBUGS. With increasing availability of spatial data referenced at fine spatial scales such as the postcode, the sparse-data situation and the Bayesian models and methods discussed herein should have considerable relevance to small area disease and health mapping and to spatial regression.