Publication:
In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results

Consultable a partir de

Date

2018

Director

Publisher

Springer
Acceso abierto / Sarbide irekia
Artículo / Artikulua
Versión aceptada / Onetsi den bertsioa

Project identifier

MINECO//MTM2014-51992-R/ES/recolecta
Gobierno de Navarra//Project 113

Abstract

Disease mapping studies the distribution of relative risks or rates in space and time, and typically relies on generalized linear mixed models (GLMMs) including fixed effects and spatial, temporal, and spatio-temporal random effects. These GLMMs are typically not identifiable and constraints are required to achieve sensible results. However, automatic specification of constraints can sometimes lead to misleading results. In particular, the penalized quasi-likelihood fitting technique automatically centers the random effects even when this is not necessary. In the Bayesian approach, the recently-introduced integrated nested Laplace approximations computing technique can also produce wrong results if constraints are not wellspecified. In this paper the spatial, temporal, and spatiotemporal interaction random effects are reparameterized using the spectral decompositions of their precision matrices to establish the appropriate identifiability constraints. Breast cancer mortality data from Spain is used to illustrate the ideas.

Keywords

Breast cancer, INLA, Leroux CAR prior, PQL, Space-time interactions

Department

Institute for Advanced Materials and Mathematics - INAMAT2

Faculty/School

Degree

Doctorate program

Editor version

Funding entities

This work has been supported by the Spanish Ministry of Economy and Competitiveness (project MTM2014- 51992-R), and by the Health Department of the Navarre Government (Project 113, Res.2186/2014).

© Springer-Verlag Berlin Heidelberg 2017

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