Open Access
Estimating unemployment in very small areas
(Institut d'Estadística de Catalunya-IDESCAT, 2009) Ugarte Martínez, María Dolores; Goicoa Mangado, Tomás; Militino, Ana F.; Sagaseta López, M.; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
In the last few years, European countries have shown a deep interest in applying small area techniques to produce reliable estimates at county level. However, the specificity of every European country and the heterogeneity of the available auxiliary information, make the use of a common methodology a very difficult task. In this study, the performance of several design-based, model-assisted, and model-based estimators using different auxiliary information for estimating unemployment at small area level is analyzed. The results are illustrated with data from Navarre, an autonomous region located at the north of Spain and divided into seven small areas. After discussing pros and cons of the different alternatives, a composite estimator is chosen, because of its good trade-off between bias and variance. Several methods for estimating the prediction error of the proposed estimator are also provided.
Open Access
Spatial gender-age-period-cohort analysis of pancreatic cancer mortality in Spain (1990-2013)
(Public Library of Science, 2017) Etxeberria Andueza, Jaione; Goicoa Mangado, Tomás; López Abente, Gonzalo; Riebler, Andrea; Ugarte Martínez, María Dolores; Estatistika eta Ikerketa Operatiboa; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística e Investigación Operativa; Gobierno de Navarra / Nafarroako Gobernua, 113, Res.2186/2014
Recently, the interest in studying pancreatic cancer mortality has increased due to its high lethality. In this work a detailed analysis of pancreatic cancer mortality in Spanish provinces was performed using recent data. A set of multivariate spatial gender-age-period-cohort models was considered to look for potential candidates to analyze pancreatic cancer mortality rates. The selected model combines features of APC (age-period-cohort) models with disease mapping approaches. To ensure model identifiability sum-to-zero constraints were applied. A fully Bayesian approach based on integrated nested Laplace approximations (INLA) was considered for model fitting and inference. Sensitivity analyses were also conducted. In general, estimated average rates by age, cohort, and period are higher in males than in females. The higher differences according to age between males and females correspond to the age groups [65, 70), [70, 75), and [75, 80). Regarding the cohort, the greatest difference between men and women is observed for those born between the forties and the sixties. From there on, the younger the birth cohort is, the smaller the difference becomes. Some cohort differences are also identified by regions and age-groups. The spatial pattern indicates a North-South gradient of pancreatic cancer mortality in Spain, the provinces in the North being the ones with the highest effects on mortality during the studied period. Finally, the space-time evolution shows that the space pattern has changed little over time.
Open Access
Temporal evolution of brain cancer incidence in the municipalities of Navarre and the Basque Country, Spain
(BioMed Central, 2015) Ugarte Martínez, María Dolores; Adin Urtasun, Aritz; Goicoa Mangado, Tomás; Casado, Itziar; Ardanaz, Eva; Larrañaga, Nerea; Estatistika eta Ikerketa Operatiboa; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística e Investigación Operativa; Gobierno de Navarra / Nafarroako Gobernua: proyecto 113 Res. 2186/2014
Background: Brain cancer incidence rates in Spain are below the European’s average. However, there are two regions in the north of the country, Navarre and the Basque Country, ranked among the European regions with the highest incidence rates for both males and females. Our objective here was two-fold. Firstly, to describe the temporal evolution of the geographical pattern of brain cancer incidence in Navarre and the Basque Country, and secondly, to look for specific high risk areas (municipalities) within these two regions in the study period (1986–2008). Methods: A mixed Poisson model with two levels of spatial effects is used. The model also included two levels of spatial effects (municipalities and local health areas). Model fitting was carried out using penalized quasi-likelihood. High risk regions were detected using upper one-sided confidence intervals. Results: Results revealed a group of high risk areas surrounding Pamplona, the capital city of Navarre, and a few municipalities with significant high risks in the northern part of the region, specifically in the border between Navarre and the Basque Country (Gipuzkoa). The global temporal trend was found to be increasing. Differences were also observed among specific risk evolutions in certain municipalities. Conclusions: Brain cancer incidence in Navarre and the Basque Country (Spain) is still increasing with time. The number of high risk areas within those two regions is also increasing. Our study highlights the need of continuous surveillance of this cancer in the areas of high risk. However, due to the low percentage of cases explained by the known risk factors, primary prevention should be applied as a general recommendation in these populations.
Open Access
Hybrid pine (Pinus attenuata × Pinus radiata) somatic embryogenesis: what do you prefer, mother or nurse?
(MDPI, 2021) Montalbán, Itziar A.; Castander Olarieta, Ander; Hargreaves, Cathy L.; Gough, Keiko; Reeves, Cathie B.; Ballekom, Shaf van; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Moncaleán, Paloma; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Development of hybrid pines of Pinus radiata D. Don for commercial forestry presents an opportunity to diversify the current resource of plant material. Climate change and different land uses pose challenges, making alternative species necessary to guarantee wood and non-wood products in the future. Pinus radiata var. cedrosensis × Pinus attenuata hybrid possesses different attributes, such as tolerance to drought conditions, better growth and resistance to snow damage at higher altitudes, and more importantly, different wood quality characteristics. Embryogenic cell lines were successfully initiated reciprocal hybrids using as initial explants megagametophytes, excised zygotic embryos and excised zygotic embryos plus nurse culture. However, the questions raised were: does the initiation environment affect the conversion to somatic plantlets months later? Does the mother tree or the cross have an effect on the conversion to somatic plantlets? In the present work we analysed the maturation rate, number of somatic embryos, germination rate, and the ex-vitro growth in cell lines derived from different initiation treatments, mother tree species, and crosses. Differences were not observed for in vitro parameters such as maturation and germination. However, significant differences were observed due to the mother tree species in relation with the ex-vitro growth rates observed, being higher those in which P. radiata acted as a mother. Moreover, embryogenic cell lines from these hybrids were stored at −80◦C and regenerated after one and five years.
Open Access
In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results
(Springer, 2018) Goicoa Mangado, Tomás; Adin Urtasun, Aritz; Ugarte Martínez, María Dolores; Hodges, James S.; Institute for Advanced Materials and Mathematics - INAMAT2
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.
Open Access
Bayesian inference in multivariate spatio-temporal areal models using INLA: analysis of gender-based violence in small areas
(Springer, 2020) Vicente Fuenzalida, Gonzalo; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
Multivariate models for spatial count data are currently receiving attention in disease mapping to model two or more diseases jointly. They have been thoroughly studied from a theoretical point of view, but their use in practice is still limited because they are computationally expensive and, in general, they are not implemented in standard software to be used routinely. Here, a new multivariate proposal, based on the recently derived M models for spatial data, is developed for spatio-temporal areal data. The model takes account of the correlation between the spatial and temporal patterns of the phenomena being studied, and it also includes spatio-temporal interactions. Though multivariate models have been traditionally fitted using Markov chain Monte Carlo techniques, here we propose to adopt integrated nested Laplace approximations to speed up computations as results obtained using both fitting techniques were nearly identical. The techniques are used to analyse two forms of crimes against women in India. In particular, we focus on the joint analysis of rapes and dowry deaths in Uttar Pradesh, the most populated Indian state, during the years 2001-2014.
Open Access
The effect of changing temperature and agar concentration at proliferation stage in the final success of Aleppo pine somatic embryogenesis
(Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA), 2017) Pereira, Catia; Montalbán, Itziar A.; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Correia, Sandra; Canhoto, Jorge M.; Moncaleán, Paloma; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
Aim of the study: The effect of physical and chemical conditions at proliferation stage was evaluated in order to elucidate if this stage is the determinant phase to induce a marked effect in Pinus halepensis somatic embryogenesis. Area of study: The study was conducted in research laboratories of Neiker (Arkaute, Spain). Material and methods: Pinus halepensis embryonal masses from ten embryogenic cell lines subjected to nine treatments (tissues cultured at three temperatures on media supplemented with three agar concentrations) at proliferation stage. Main results: Significant differences were observed among different proliferation conditions months later at the end of maturation, germination and acclimatization stages. Research highlights: Aleppo pine embryonal masses are cultured under standard conditions on a culture medium supplemented with 4.5 g/L Gelrite® at 23ºC. However, better results in terms of plantlet production can be obtained proliferating the embryonal masses at 18ºC in a culture media with significantly lower water availability.
Open Access
La necesidad de un manifiesto a favor de la alfabetización estadística: editorial
(Sociedad Estadística e Investigación Operativa (SEIO), 2025-03-27) Casals, Martí ; Daunis i Estadella, Pepus; Galé, Carmen; Goicoa Mangado, Tomás; Patino-Alonso, Carmen; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2
Imagina leer el siguiente titular en un periódico: “Las personas que consumen café tienen un 50% menos de probabilidad de desarrollar ciertas enfermedades”. A partir de esta lectura, decides aumentar tu consumo diario de café. Sin embargo, antes de tomar esa decisión hay que conocer cómo se ha llegado a dicha conclusión: cuál es el tamaño de la muestra, el diseño del estudio, sobre qué población se aplica, el método de recolección de datos, y valorar si es posible establecer relaciones causales. Este ejemplo ilustra como la falta de conocimiento sobre el procedimiento puede comprometer la validez del estudio y se puede traducir en una interpretación incorrecta o unos resultados sesgados. Una comunicación deficiente puede llevar decisiones mal fundamentadas, incluso en áreas críticas como la salud y el bienestar (Spiegelhalter, 2019).
Open Access
Comments on: Modular regression - a Lego system for building structured additive distributional regression models with tensor product interactions
(Springer, 2019) Goicoa Mangado, Tomás; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
This paper comments the article 'Modular regression - a Lego system for building structured additive distributional regression models with tensor product interactions', where the authors address the important topic of building very general models with interaction terms facing the relevant issue of identifiability.
Open Access
High-dimensional order-free multivariate spatial disease mapping
(Springer, 2023) Vicente Fuenzalida, Gonzalo; Adin Urtasun, Aritz; 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, PJUPNA2001
Despite the amount of research on disease mapping in recent years, the use of multivariate models for areal spatial data remains limited due to difficulties in implementation and computational burden. These problems are exacerbated when the number of areas is very large. In this paper, we introduce an order-free multivariate scalable Bayesian modelling approach to smooth mortality (or incidence) risks of several diseases simultaneously. The proposal partitions the spatial domain into smaller subregions, fits multivariate models in each subdivision and obtains the posterior distribution of the relative risks across the entire spatial domain. The approach also provides posterior correlations among the spatial patterns of the diseases in each partition that are combined through a consensus Monte Carlo algorithm to obtain correlations for the whole study region. We implement the proposal using integrated nested Laplace approximations (INLA) in the R package bigDM and use it to jointly analyse colorectal, lung, and stomach cancer mortality data in Spanish municipalities. The new proposal allows for the analysis of large datasets and yields superior results compared to fitting a single multivariate model. Additionally, it facilitates statistical inference through local homogeneous models, which may be more appropriate than a global homogeneous model when dealing with a large number of areas.