Open Access
scmamp: statistical comparison of multiple algorithms in multiple problems
(The R Foundation, 2016) Calvo, Borja; Santafé Rodrigo, Guzmán; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
Comparing the results obtained by two or more algorithms in a set of problems is a central task in areas such as machine learning or optimization. Drawing conclusions from these comparisons may require the use of statistical tools such as hypothesis testing. There are some interesting papers that cover this topic. In this manuscript we present scmamp, an R package aimed at being a tool that simplifies the whole process of analyzing the results obtained when comparing algorithms, from loading the data to the production of plots and tables. Comparing the performance of different algorithms is an essential step in many research and practical computational works. When new algorithms are proposed, they have to be compared with the state of the art. Similarly, when an algorithm is used for a particular problem, its performance with different sets of parameters has to be compared, in order to tune them for the best results. When the differences are very clear (e.g., when an algorithm is the best in all the problems used in the comparison), the direct comparison of the results may be enough. However, this is an unusual situation and, thus, in most situations a direct comparison may be misleading and not enough to draw sound conclusions; in those cases, the statistical assessment of the results is advisable. The statistical comparison of algorithms in the context of machine learning has been covered in several papers. In particular, the tools implemented in this package are those presented in Demšar (2006); García and Herrera (2008); García et al. (2010). Another good review that covers, among other aspects, the statistical assessment of the results in the context of supervised classification can be found in Santafé et al. (2015).
Open Access
Association of intrinsic capacity with incidence and mortality of cardiovascular disease: prospective study in UK Biobank
(Wiley, 2023) Ramírez Vélez, Robinson; Iriarte-Fernández, María; Santafé Rodrigo, Guzmán; Malanda Trigueros, Armando; Beard, John R.; García Hermoso, Antonio; Izquierdo Redín, Mikel; Ciencias de la Salud; Estadística, Informática y Matemáticas; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute for Advanced Materials and Mathematics - INAMAT2; Osasun Zientziak; Estatistika, Informatika eta Matematika; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Background: The World Health Organization proposed the concept of intrinsic capacity (IC; the composite of all the physical and mental capacities of the individual) as central for healthy ageing. However, little research has investigated the interaction and joint associations of IC with cardiovascular disease (CVD) incidence and CVD mortality in middle- and older-aged adults. Methods: Using data from 443 130 UK Biobank participants, we analysed seven biomarkers capturing the level of functioning of five domains of IC to calculate a total IC score (ranging from 0 [better IC] to +4 points [poor IC]). Associations between IC score and incidence of six long-term CVD conditions (hypertension, stroke/transient ischaemic attack stroke, peripheral vascular disease, atrial fibrillation/flutter, coronary artery disease and heart failure), and grouped mortality from these conditions were estimated using Cox proportional models, with a 1-year landmark analysis to triangulate the findings. Results: Over 10.6 years of follow-up, CVD morbidity grouped (n = 384 380 participants for the final analytic sample) was associated with IC scores (0 to +4): mean hazard ratio (HR) [95% confidence interval, CI] 1.11 [1.08–1.14], 1.20 [1.16–1.24], 1.29 [1.23–1.36] and 1.56 [1.45–1.59] in men (C-index = 0.68), and 1.17 [1.13–1.20], 1.30 [1.26–1.36], 1.52 [1.45–1.59] and 1.78 [1.67–1.89] in women (C-index = 0.70). In regard to mortality, our results indicated that the higher IC score (+4 points) was associated with a significant increase in subsequent CVD mortality (mean HR [95% CI]: 2.10 [1.81–2.43] in men [C-index = 0.75] and 2.29 [1.85–2.84] in women [C-index = 0.78]). Results of all sensitivity analyses by full sample, sex and age categories were largely consistent independent of major confounding factors (P < 0.001). Conclusions: IC deficit score is a powerful predictor of functional trajectories and vulnerabilities of the individual in relation to CVD incidence and premature death. Monitoring an individual's IC score may provide an early-warning system to initiate preventive efforts.
Open Access
Association of intrinsic capacity with respiratory disease mortality
(Elsevier, 2023) Ramírez Vélez, Robinson; Iriarte-Fernández, María; Santafé Rodrigo, Guzmán; Malanda Trigueros, Armando; Beard, John R.; García Hermoso, Antonio; Izquierdo Redín, Mikel; Ciencias de la Salud; Estadística, Informática y Matemáticas; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute for Advanced Materials and Mathematics - INAMAT2; Osasun Zientziak; Estatistika, Informatika eta Matematika; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
The World Health Organization (WHO) introduced a framework for healthy aging in 2015 that emphasizes functional ability instead of absence of disease. Healthy ageing is defined as “the process of building and maintaining the functional ability that enables well-being”. This framework considers an individual’s intrinsic capacity (IC), environment, and the interaction between them to determine functional ability. In this prospective cohort study, we investigated the link between mortality and various respiratory diseases in almost half a million adults who are part of the UK Biobank. We derived an IC score using measures from 4 of the 5 domains: two for psychological capacity, two for sensory capacity, two for vitality and one for locomotor capacity. The exposure variable in the study was the number of reported factors, which was summed and categorized into IC scores of zero, one, two, three, or at least four. The outcome was respiratory disease-related mortality, which was linked to national mortality records. The follow-up period started from participants’ inclusion in the UK Biobank study (2006–2010) and ended on December 31, 2021, or the participant’s death was censored. The average follow-up was 10.6 years (IQR 10.0; 11.3). During a median follow-up period of 10.6 years, 27,251 deaths were recorded. Out of these, 7.5% (2059) were primarily attributed to respiratory disease. The results showed that a higher IC score (+4 points) was associated with a significantly increased risk of respiratory disease mortality, with HRs of 3.34 [2.64 to 4.23] for men (C-index = 0.83) and 3.87 [2.86 to 5.23] for women (C-index = 0.84), independent of major confounding factors (P < 0.001). Our study provides evidence that lower levels of the WHO’s IC construct are associated with increased risk of mortality and various adverse health outcomes. The IC construct, which is easily and inexpensively measured, holds great promise for transforming geriatric care worldwide, including in regions without established geriatric medicine.
Open Access
Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences
(Springer, 2024) Echegoyen Arruti, Carlos; Pérez, Aritz; Santafé Rodrigo, Guzmán; Pérez Goya, Unai; 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
Temporal sequences of satellite images constitute a highly valuable and abundant resource for analyzing regions of interest. However, the automatic acquisition of knowledge on a large scale is a challenging task due to different factors such as the lack of precise labeled data, the definition and variability of the terrain entities, or the inherent complexity of the images and their fusion. In this context, we present a fully unsupervised and general methodology to conduct spatio-temporal taxonomies of large regions from sequences of satellite images. Our approach relies on a combination of deep embeddings and time series clustering to capture the semantic properties of the ground and its evolution over time, providing a comprehensive understanding of the region of interest. The proposed method is enhanced by a novel procedure specifically devised to refine the embedding and exploit the underlying spatio-temporal patterns. We use this methodology to conduct an in-depth analysis of a 220 km region in northern Spain in different settings. The results provide a broad and intuitive perspective of the land where large areas are connected in a compact and well-structured manner, mainly based on climatic, phytological, and hydrological factors.
Open Access
Dealing with risk discontinuities to estimate cancer mortality risks when the number of small areas is large
(SAGE, 2021-02-17) Santafé Rodrigo, Guzmán; Adin Urtasun, Aritz; Lee, Duncan; Ugarte Martínez, María Dolores; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2
Many statistical models have been developed during the last years to smooth risks in disease mapping. However, most of these modeling approaches do not take possible local discontinuities into consideration or if they do, they are computationally prohibitive or simply do not work when the number of small areas is large. In this paper, we propose a two-step method to deal with discontinuities and to smooth noisy risks in small areas. In a first stage, a novel density-based clustering algorithm is used. In contrast to previous proposals, this algorithm is able to automatically detect the number of spatial clusters, thus providing a single cluster structure. In the second stage, a Bayesian hierarchical spatial model that takes the cluster configuration into account is fitted, which accounts for the discontinuities in disease risk. To evaluate the performance of this new procedure in comparison to previous proposals, a simulation study has been conducted. Results show competitive risk estimates at a much better computational cost. The new methodology is used to analyze stomach cancer mortality data in Spanish municipalities.
Open Access
Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach
(Springer, 2022) Adin Urtasun, Aritz; Congdon, P.; Santafé Rodrigo, Guzmán; Ugarte Martínez, María Dolores; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important.