Structure induced by a multiple membership transformation on the Conditional Autoregressive model

TL;DR

This paper explores the theoretical foundations and practical implications of applying a multiple membership transformation to the Conditional Autoregressive (CAR) model for disease mapping, especially when data sources are spatially misaligned.

Contribution

It provides a detailed theoretical analysis of the multiple membership CAR model, including parameterisation, properness, and identifiability, along with simulation studies and a real-world application.

Findings

Parameters are identifiable under specific conditions.

Simulation results inform practical model implementation.

Application to South London diabetes data demonstrates model utility.

Abstract

The objective of disease mapping is to model data aggregated at the areal level. In some contexts, however, (e.g. residential histories, general practitioner catchment areas) when data is arising from a variety of sources, not necessarily at the same spatial scale, it is possible to specify spatial random effects, or covariate effects, at the areal level, by using a multiple membership principle (MM) (Petrof et al. 2020, Gramatica et al. 2021). A weighted average of conditional autoregressive (CAR) spatial random effects embeds spatial information for a spatially-misaligned outcome and estimate relative risk for both frameworks (areas and memberships). In this paper we investigate the theoretical underpinnings of these application of the multiple membership principle to the CAR prior, in particular with regard to parameterisation, properness and identifiability. We carry out simulations involving different numbers of memberships as compared to number of areas and assess impact of this on estimating parameters of interest. Both analytical and simulation study results show under which conditions parameters of interest are identifiable, so that we can offer actionable recommendations to practitioners. Finally, we present the results of an application of the multiple membership model to diabetes prevalence data in South London, together with strategic implications for public health considerations