When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available.

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium / LA SERRA, Vittoria; Faes, Christel; Hens, Niel; Brutti, Pierpaolo. - (2020), pp. 721-726. (Intervento presentato al convegno 50th Meeting of the Italian Statistical Society tenutosi a Pisa).

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

Vittoria La Serra
Primo
;
Pierpaolo Brutti
Ultimo
2020

Abstract

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available.
2020
50th Meeting of the Italian Statistical Society
spatial correlation; autoregressive models; CAR, DAGAR; epidemiology; diabetes
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium / LA SERRA, Vittoria; Faes, Christel; Hens, Niel; Brutti, Pierpaolo. - (2020), pp. 721-726. (Intervento presentato al convegno 50th Meeting of the Italian Statistical Society tenutosi a Pisa).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1480718
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