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Urdangarin Iztueta, Arantxa

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https://academica-e.unavarra.es/handle/2454/50107

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Urdangarin Iztueta

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Arantxa

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Estadística, Informática y Matemáticas

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InaMat2. Instituto de Investigación en Materiales Avanzados y Matemáticas

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0000-0002-9101-6459

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811962

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2022 - 20244
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Author: Urdangarin Iztueta, Arantxa × End date: 2024 ×

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Now showing 1 - 4 of 4
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    PublicationOpen Access
    Advances in the estimation of fixed effects in spatial models with random effects
    (2024) Urdangarin Iztueta, Arantxa; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    La representación cartográfica de enfermedades permite estimar indicadores de salud específicos para áreas geográficas dentro de una región de estudio. Aunque el objetivo principal suele ser proporcionar las tasas/riesgos de incidencia o mortalidad de enfermedades como el cáncer, existen otras aplicaciones. Por ejemplo, el análisis de crímenes contra las mujeres en India. La mayor parte de la investigación en la representación cartográfica de enfermedades usa modelos mixtos de Poisson jerárquicos bayesianos que incorporan la dependencia espacial o temporal para suavizar los riesgos y reducir la variabilidad de los estimadores clásicos de los riesgos como las razones de incidencia/mortalidad estandarizadas (RIE/RME). Sin embargo, los modelos de representación cartográfica de enfermedades tienen algunos inconvenientes. Aquí nos centramos en dos de estas limitaciones. En primer lugar, estos modelos en general no son identificables y se requieren restricciones en el proceso de estimación para obtener resultados razonables. El segundo problema es la confusión espacial y está relacionado con la inclusión de covariables en los modelos. Si las covariables tienen estructura espacial, su asociación con la respuesta puede no estimarse bien debido al sesgo y la inflación de la varianza. El objetivo principal de esta tesis es doble. Por un lado, abordaremos la complejidad de incorporar restricciones de suma cero para resolver los problemas de identificación al ajustar modelos espacio-temporales ampliamente utilizados en la representación cartográfica de enfermedades utilizando NIMBLE (de Valpine et al., 2017), un sistema para crear modelos estadísticos en R que permite ajustar modelos jerárquicos bayesianos utilizando un sistema configurable de algoritmos MCMC. Por otro lado, nos centraremos en la confusión espacial, con el objetivo de proponer un método que garantice estimaciones adecuadas de efectos fijos. La presente tesis está dividida en cuatro capítulos diferentes. El primer capítulo proporciona una introducción general sobre los problemas que se van a bordar en esta tesis y el resto de los capítulos profundizan en esos problemas. Esta tesis se cierra con una sección final que resume los principales resultados e introduce algunas ideas para futuras investigaciones.
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    PublicationOpen Access
    Evaluating recent methods to overcome spatial confounding
    (Springer, 2022) Urdangarin Iztueta, Arantxa; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
    The concept of spatial confounding is closely connected to spatial regression, although no general definition has been established. A generally accepted idea of spatial confounding in spatial regression models is the change in fixed effects estimates that may occur when spatially correlated random effects collinear with the covariate are included in the model. Different methods have been proposed to alleviate spatial confounding in spatial linear regression models, but it is not clear if they provide correct fixed effects estimates. In this article, we consider some of those proposals to alleviate spatial confounding such as restricted regression, the spatial+ model, and transformed Gaussian Markov random fields. The objective is to determine which one provides the best estimates of the fixed effects. Dowry death data in Uttar Pradesh in 2001, stomach cancer incidence data in Slovenia in the period 1995–2001 and lip cancer incidence data in Scotland between the years 1975–1980 are analyzed. Several simulation studies are conducted to evaluate the performance of the methods in different scenarios of spatial confounding. Results reflect that the spatial+ method seems to provide fixed effects estimates closest to the true value although standard errors could be inflated
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    PublicationOpen Access
    A simplified spatial+ approach to mitigate spatial confounding in multivariate spatial areal models
    (Elsevier, 2024) Urdangarin Iztueta, Arantxa; Goicoa Mangado, Tomás; Kneib, Thomas; Ugarte Martínez, María Dolores; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2
    Spatial areal models encounter the well-known and challenging problem of spatial confounding. This issue makes it arduous to distinguish between the impacts of observed covariates and spatial random effects. Despite previous research and various proposed methods to tackle this problem, finding a definitive solution remains elusive. In this paper, we propose a simplified version of the spatial+ approach that involves dividing the covariate into two components. One component captures large-scale spatial dependence, while the other accounts for short-scale dependence. This approach eliminates the need to separately fit spatial models for the covariates. We apply this method to analyse two forms of crimes against women, namely rapes and dowry deaths, in Uttar Pradesh, India, exploring their relationship with socio-demographic covariates. To evaluate the performance of the new approach, we conduct extensive simulation studies under different spatial confounding scenarios. The results demonstrate that the proposed method provides reliable estimates of fixed effects and posterior correlations between different responses.
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    PublicationOpen Access
    Space-time interactions in bayesian disease mapping with recent tools: making things easier for practitioners
    (Edward Arnold, 2022) Urdangarin Iztueta, Arantxa; Ugarte Martínez, María Dolores; Goicoa Mangado, Tomás; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
    Spatio-temporal disease mapping studies the distribution of mortality or incidence risks in space and its evolution in time, and it usually relies on fitting hierarchical Poisson mixed models. These models are complex for practitioners as they generally require adding constraints to correctly identify and interpret the different model terms. However, including constraints may not be straightforward in some recent software packages. This paper focuses on NIMBLE, a library of algorithms that contains among others a configurable system for Markov chain Monte Carlo (MCMC) algorithms. In particular, we show how to fit different spatio-temporal disease mapping models with NIMBLE making emphasis on how to include sum-to-zero constraints to solve identifiability issues when including spatio-temporal interactions. Breast cancer mortality data in Spain during the period 1990-2010 is used for illustration purposes. A simulation study is also conducted to compare NIMBLE with R-INLA in terms of parameter estimates and relative risk estimation. The results are very similar but differences are observed in terms of computing time.