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The single-index model is a non-parametric model that is popular in statistics for the estimation of risk assessment, thanks to its flexibility. Many studies have approached the problem of finding the best estimators for the model parameters and smooth function. However, most of the real-world data is asymmetric. In this thesis, we consider the application of a Box-Cox transformation on the response variable to improve the performances of the single-index model. Indeed, the Box-Cox transformation addresses the skewness of the response and transform it into a normally-distributed variables. We consider also the log transformation of the response variable. We show through a simulation study that the parameters of the models fitted with the transformed response have more similarity with the true parameters, which shows that the model is more reliable. Finally, we perform a study on a real-world data set from the car insurance industry, where we build a single-index model to predict the expected cost per claim based on a set of variables, that concern the demographic characteristic and the driving style of the customer. We assess the performance of the model fitted on the original data as well the models that use the log and the Box-Cox transformation, that obtain better results than the original one.

The mental health landscape worldwide has been profoundly affected by the COVID-19 pandemic, with an escalating rise in stress, anxiety, and depression. As a result, the urgency to create and apply efficacious methods for averting and addressing these mental health issues has never been greater. In this context COVICAT is a research project that is being carried out in Catalonia, Spain. The project is led by the Germans Trias i Pujol Research Institute (IGTP) and the Barcelona Institute for Global Health (ISGlobal). The goal of COVICAT is to evaluate the impact of the COVID-19 pandemic on the Catalan population. This master's thesis aims to leverage machine learning methodologies for psychological risk assessment using the COVICAT dataset (N = 9,398), contributing to healthcare practices by facilitating the initial classification of patients requiring psychological intervention. The study focuses on predicting four outcome variables reflecting patients' psychological wellbeing, one of which encapsulates a patient's self-perceived mental state. Initial exploratory data analysis and data engineering were performed to prepare the dataset for machine learning applications, including bagging, boosting, and stacking models. Each model underwent an analogous pipeline with minor variations, dictated by the distinct nature of the respective model. Post-training, these models were evaluated on unseen data, generating several performance metrics. The F1-score, designated as the critical criterion by the subject matter experts, was employed to gauge the models' efficacy. Based on these evaluations, the optimal predictive model was selected for each target. The resulting models delivered performance surpassing the adequate standard established initially by the experts (F1 = 0.6). These models aim to augment, not supplant, professional healthcare diagnoses by enabling efficient patient triage for psychological assistance.

This paper provides a simulation and optimisation-based system to combine public transport (PT) with ride-pooling services (RP). According to the International Transport Forum (ITF), the RP could be established as a feeder of PT and included as the first or last leg of the journey with the option of transferring to/from PT in between. The system contains a dispatching core that uses an optimisation model with heuristic parameters to quickly analyse the potential permutations for each request. This topic is frequently based on simplistic modelling in the literature, and it has not been extensively tested in major urban regions. The whole metropolitan region of Barcelona is employed in this study, with a large realistic simulation model encompassing a 20 × 15 km area with a PT network of about 3000 stations and 300 route lines and nearly 114,000 traffic links. This enables for a more accurate evaluation of system performance and trip quality computation. This research was funded by an Industrial PhD by the Generalitat de Catalunya and the company PTV-AG under Grant DI-071 2019; and a research project of the Spanish R+D Programs under Grant PID2020-112967GB-C31.

La iniciativa STROBE (STrengthening the Reporting of Observational studies in Epidemiology) fue publicada en el año 2007, y consta de una serie de directrices y recomendaciones dedicadas a mejorar la replicabilidad de los estudios observacionales. Una serie de extensiones adaptadas a distintas disciplinas académicas se han ido publicando, pero ninguna modificación (quitando alguna iniciativa sin mucho éxito como STRATOS) del documento base se ha realizado desde su publicación original. Múltiples estudios han intentado valorar el grado de mejoría en la implementación de estas directrices en distintas muestras de artículos, pero estas metodologías implican una serie de limitaciones que dificultan su propia replicabilidad. Después de más de una década de su publicación queremos desarrollar una metodología que nos permita valorar diferencias entre el grado de implementación de las directrices antes y después de la publicación de STROBE con un mayor grado de replicabilidad. Esto se ha hecho a partir de una muestra de 100 artículos dividida en dos franjas temporales (antes y después de la publicación), en los que se ha hecho una evaluación de una selección de 5 puntos de STROBE mediante preguntas binarias para generar unas puntuaciones para cada punto analizado. La muestra fue obtenida mediante las herramientas electrónicas de PubMed, para extraer una serie de parámetros de interés como el año de publicación o el número de citas del artículo. Posteriormente, para facilitar el traspaso de los datos, se hizo uso de un código SQL que permitiera el traspaso de la información de las evaluaciones a documentos CSV, para ser procesados mediante el programa “Rstudio”. A partir de las puntuaciones se ha realizado un análisis de componentes principales (PCA) y un análisis de correspondencias múltiples (MCA) para analizar el comportamiento de cada uno de los puntos y respuestas. Asimismo, hemos planteado un método de inferencia estadística mediante un test de permutaciones MonteCarlo para poder diferenciar las puntuaciones entre los dos grupos. Analizando los resultados obtenidos, no tenemos las evidencias suficientes para poder afirmar que STROBE, el paso del tiempo u otros factores extrínsecos directamente asociados a STROBE, haya provocado un cambio con el paso del tiempo. Cabe destacar que la finalidad de este TFM es la del desarrollo metodológico, no de extracción de conclusiones debido a las limitaciones del propio. Por lo tanto, consideramos necesario el refinado y verificación de esta metodología para potenciar su uso futuro.

El objetivo de este trabajo es estudiar la asociación entre las medidas antropométricas y la actividad física, y el riesgo de desarrollar un cáncer de cuello uterino o uno de sus precursores. A partir de la base de datos del estudio EPIC (European Prospective Investigation into Cancer and Nutrition) se realizarán varios tipos de análisis: modelos de riesgo proporcional de Cox para estimar hazard ratio y modelos de regresión logística condicional para estimar odds ratios. Además, se validarán ambos tipos de modelos, en concreto, el de regresión logística condicional mediante un ajuste de bondad específico para datos emparejados.

Treballs Finals de Grau en Estadística UB-UPC, Facultat d'Economia i Empresa (UB) i Facultat de Matemàtiques i Estadística (UPC), Curs: 2021-2022, Tutor: Jose Bonifacio Saez Madrid, L’auge de les criptomonedes és un fet històric i que cal estudiar més enllà dels vessants tecnològic i social. Així doncs, al llarg de l’informe s’analitzarà el mercat de les criptomonedes tot emprant eines estadístiques i models econòmics. Es començarà creant un índex representatiu del mercat, i se l’analitzarà a la vegada que es va comparant amb els principals mercats borsaris. Més tard, s’utilitzaran eines de predicció per veure fins a quin punt és possible pronosticar el que passarà al mercat a curt termini tot utilitzant dades del que ha succeït en el passat tant en el mercat de les criptomonedes com en els mercats tradicionals. Per últim, es veurà si emprant diferents models d’optimització de carteres per a un inversor és preferible invertir en el mercat de forma passiva o activa, i es veurà si es pot crear alguna cartera amb una volatilitat relativament semblant als mercats tradicionals però que a la vegada tingui una elevada rendibilitat esperada, tret clau del mercat de les criptomonedes.

Objective Direct cortical stimulation (DCS) is standard for intracranial presurgical evaluation in drug-resistant epilepsy (DRE). Few studies have reported levels of concordance between spontaneous seizure generators and triggered seizures during DCS. The present work reports validity measures of DCS for detecting the seizure onset zone (SOZ) during stereoelectroencephalography (SEEG). Methods We evaluated all patients who underwent SEEG evaluation at our epilepsy center between 2013 and 2019. Data were analyzed using contingency tables. Validity measures of the diagnostic test were computed for all patients evaluated with DCS and for seizure free patients. Results Fifty-eight consecutive patients were evaluated through DCS. One hundred seventy-three clinical seizures were elicited with DCS. Electroclinical identical to spontaneous seizures were considered true positive (TP) seizures. They showed a high specificity (96.9%) for detecting the SOZ in patients that remained seizure free one year after treatment. Sensitivity was low (23.0%), and a high percentage of false-negative stimulations was documented in the SOZ. The accuracy was 87.9%. Conclusions DCS is a technique with high specificity but a low sensitivity for the localization of the SOZ. The DCS validity measures need to be known when considered for surgical decisions. The interpretation of DCS-triggered seizures and the differentiation of true-positive vs false-positive seizures should be carefully evaluated. Significance DCS seizure triggering is highly specific for SOZ localization. This work received support from the project “Clúster Emergent del Cervell Humà” (CECH) and the European Regional Development Fund under the framework of the ERFD Operative Programme for Catalonia.

Treballs Finals de Grau en Estadística UB-UPC, Facultat d'Economia i Empresa (UB) i Facultat de Matemàtiques i Estadística (UPC), Curs: 2021-2022, Tutor: Daniel Fernández Martínez i Martí Casals Toquero, L’estadística descriptiva i concretament les tècniques clustering són de les més aplicades en les ciències de l’esport. Aquest treball presenta una revisió sistemàtica (RS) d’articles originals que utilitzen les tècniques clústering en l’àmbit de les ciències de l’esport seguint la guia PRISMA. La cerca es va realitzar en diferents bases de dades amb les paraules clau i booleans següents: “clustering” and “sport”. Un total de 54 articles van ser seleccionats, on el 85% d’ells es van publicar els últims 7 anys, i el 50% d’aquests estudiaven el rendiment esportiu. Els participants d’aquests estudis són majoritàriament atletes professionals (61,1%), relacionats amb esports com el futbol (soccer) (16,7%), bàsquet (7,4%) i tennis (11,1%), respectivament. El 35% dels articles utilitzaven el clustering jeràrquic mentre que el mètode més utilitzat va ser el k-means (clustering particional). En el 31,3 % dels articles no es va reportar el mètode clustering utilitzat, ni el mètode per decidir el nombre de clústers (35,2%), ni en més d’un 70% aspectes computacionals i relacionats amb la reproductibilitat de les dades. A més a més, en aquest treball es posa en pràctica una de les tècniques de clustering particional més popular per estudiar el perfil de les jugadores de la lliga professional de bàsquet femenina dels Estats Units des de l’any 1997 fins al 2019.

Treballs Finals de Grau en Estadística UB-UPC, Facultat d'Economia i Empresa (UB) i Facultat de Matemàtiques i Estadística (UPC), Curs: 2021-2022, Tutor: David Moriña Soler, Aquest és un estudi de la segona enquesta catalana de les condicions de treball (II ECCT) realitzada l'any 2010. L'enquesta es basa en el mètode COPSOQ desenvolupat a Dinamarca l'any 2000. En el treball, es busca, per una banda, confirmar que les dades observades s'ajusten al model de dimensions teoritzat, per fer-ho s'ha utilitzat una anàlisi factorial confirmatòria (CFA). Amb l'ajuda d'aquest mètode s'ha pogut confirmar el model teoritzat a partir de les dades observades. També ha permès obviar una qüestió que no aportava nova informació, ja que les respostes eren molt similars a l'anterior. D'altra banda, amb aquest estudi es vol brindar una eina que ajudi a predir quan un individu pot estar en risc de patir una malaltia mental en funció al que hagi respost. Per fer-ho s'ha aplicat a les dades l'algoritme "Random forest" que prediu quan un individu està en risc de patir una malaltia de salut mental pràcticament en un 75% dels casos.

La gaviota de Audouin (Larus audouinii) es un ave marina que se encuentra en estado vulnerable debido a una rápida disminución de su población. Estudios anteriores revelaron que la población de la colonia del delta del Ebro ha sabido adaptar su comportamiento a la actividad pesquera, aunque es posible que esta adaptación haya ocurrido de forma desigual en los individuos de la colonia. En este trabajo se analizan los datos de las trayectorias de 36 gaviotas de Audouin de la colonia del delta del Ebro, 38090 localizaciones espaciotemporales recogidas con dispositivos GPS, con el objetivo de evaluar la posible existencia de subpoblaciones de gaviotas. Primero, se desarrolla e implementa un algoritmo eficiente para el cálculo de la distancia de Hausdorff mediante el software R, logrando reducir notablemente el tiempo computacional de la operación. Luego, se lleva a cabo un análisis clúster segregando las trayectorias por los diferentes momentos de la actividad pesquera (presencia diurna, presencia nocturna y ausencia de pesca) con los que se consigue identificar gaviotas con comportamientos extremos y se confirma la necesidad de la segregación.

The spatial arrangement of a metropolis is of utmost importance to carry out daily activities, which are constrained by space and time. Accessibility is not only shaped by the spatial and temporal dimension, but it is also defined by individual characteristics, such as gender, impairments, or socioeconomic characteristics of the citizens living or commuting in this area. This study analyzes mobility trends and patterns in the metropolitan area of Barcelona before and after the COVID-19 pandemic outbreak, with special emphasis on gender and equality. The study draws on multiple sources of information, however, two main datasets are analyzed: two traditional travel surveys from the transport metropolitan area of Barcelona and two coming from smartphone data. The results show that gender plays a relevant role when analyzing mobility patterns, as already highlighted in other studies, but, after the pandemic outbreak, some population groups were more likely to change their mobility patterns, for example, highly educated population groups and those with higher income. This study also highlights that e-activities may shape new mobility patterns and living conditions for some population segments, but some activities cannot be replaced by IT technologies. For all these reasons, city and transport planning should foster sustainable development policies, which will provide the maximum benefit for society.

In this paper, the results of the project INSESS-COVID19 are presented, as part of a special call owing to help in the COVID19 crisis in Catalonia. The technological infrastructure and methodology developed in this project allows the quick screening of a territory for a quick a reliable diagnosis in front of an unexpected situation by providing relevant decisional information to support informed decision-making and strategy and policy design. One of the challenges of the project was to extract valuable information from direct participatory processes where specific target profiles of citizens are consulted and to distribute the participation along the whole territory. Having a lot of variables with a moderate number of citizens involved (in this case about 1000) implies the risk of violating statistical secrecy when multivariate relationships are analyzed, thus putting in risk the anonymity of the participants as well as their safety when vulnerable populations are involved, as is the case of INSESS-COVID19. In this paper, the entire data-driven methodology developed in the project is presented and the dealing of the small subgroups of population for statistical secrecy preserving described. The methodology is reusable with any other underlying questionnaire as the data science and reporting parts are totally automatized. Peer Reviewed Objectius de Desenvolupament Sostenible::1 - Fi de la Pobresa Objectius de Desenvolupament Sostenible::5 - Igualtat de Gènere Objectius de Desenvolupament Sostenible::10 - Reducció de les Desigualtats Objectius de Desenvolupament Sostenible::3 - Salut i Benestar Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement Econòmic

The spread of the SARS-CoV-2 modeling is a challenging problem because of its complex nature and lack of information regarding certain aspects. In this paper, we explore a Digital Twin approach to model the pandemic situation in Catalonia. The Digital Twin is composed of three different dynamic models used to perform the validations by a Model Comparison approach. We detail how we use this approach to obtain knowledge regarding the effects of the nonpharmaceutical interventions and the problems we faced during the modeling process. We use Specification and Description Language (SDL) to represent the compartmental forecasting model for the SARS-CoV-2. Its graphical notation simplifies the different specialists’ understanding of the model hypotheses, which must be validated continuously following a Solution Validation approach. This model allows the successful forecasting of different scenarios for Catalonia. We present some formalization details, discuss the validation process and present some results obtained from the validation model discussion, which becomes a digital twin of the pandemic in Catalonia.

Treballs Finals de Grau en Estadística UB-UPC, Facultat d'Economia i Empresa (UB) i Facultat de Matemàtiques i Estadística (UPC), Curs: 2020-2021, Tutor: Francesc de Asis Carmona Pontaque, En aquest treball es desenvolupa una tècnica de visualització i modelització de dades ecològiques, en concret, s’estudia una tècnica d’anàlisi multivariant anomenada anàlisi de la redundància des d’un punt de vista teòric, observant la metodologia que segueix i, posteriorment s’aplica a unes dades ambientals. A més, per a dur a terme aquest estudi, es fa l’anàlisi usant models lineals múltiples tradicionals analitzant totes les característiques que presenten contra el model multivariant lineal múltiple (RDA). Estudiant així, si amb un únic conjunt de variables explicatives es poden predir diferents objectius, en concret, els nivells de 𝐶𝑂2 𝑖 𝑁𝑂𝑥.

The consequences of supplementing Lys, Met, and Thr in milk replacers (MR) for calves have been widely studied, but scarce information exists about potential roles of other AA (whether essential or not). The effects on growth performance of supplementation of 4 different AA combinations in a mixed ration (25.4% crude protein and 20.3% fat) based on skim milk powder and whey protein concentrate were evaluated in 76 Holstein male calves (3 ± 1.7 d old). The 4 MR were as follows: CTRL with no AA supplementation; PG, supplying additional 0.3% Pro and 0.1% Gly; FY, supplying additional 0.2% Phe and 0.2% Tyr; and KMT, providing additional 0.62% Lys, 0.22% Met, and 0.61% Thr. All calves were fed the same milk allowance program and were weaned at 56 d of study. Concentrate intake was limited to minimize interference of potential differences in solid feed intake among treatments. Animals were weighed weekly, intakes recorded daily, and blood samples obtained at 2, 5, and 7 wk of study to determine serum urea and plasma AA concentrations. Plasma AA concentrations were explored using compositional data analysis, and their isometric log-ratio transformations were used to analyze their potential influence on ADG and serum urea concentration using a linear mixed-effects model. We detected no differences in calf performance and feed intake. Plasma relative concentration of the AA supplemented in the KMT and PG treatments increased in their respective treatments, and, in PG calves, a slight increase in the proportion of plasma Gly, Glu, and branched-chain AA was also observed. The proportions of plasma branched-chain AA, His, and Gln increased, and those of Thr, Arg, Lys, and Glu decreased with calves' age. A specific log-contrast balance formed by Arg, Thr, and Lys was found to be the main driver for lowering serum urea concentrations and increasing calf growth. The use of compositional mixed-effects models identified a cluster formed by the combination of Arg, Thr, and Lys, as a potential AA to optimize calf growth. info:eu-repo/semantics/publishedVersion

In this work I tried to use different binary classifiers to analyse audio recordings of people coughing and classify them as affected or non affected by covid-19, as has been done during the last year by different research groups all over the world. I took the raw audio data from the CoSwara repository, from which after some manipulation I extracted the mel frequency cepstral coefficients and other features, and I used the logistic regression, the support vector machine and the multilayer perceptron as classifiers to perform my supervised learning analysis. In order to overcome the problem of the imbalance of the dataset (in the training set Positive/Negative ratio was around 1/12) I used the syntetic minority oversampling technique (SMOTE). I performed the hyperparameters tuning through cross validation on the training set and then I built the final model for logistic regression and multilayer perceptron, which I assessed using a test set composed of new observations. In both stages of the work, the models have been assessed using the area under the ROC curve. Incoming

Dating is a key element for archaeologists. We propose a Bayesian approach to provide chronology to sites that have neither radiocarbon dating nor clear stratigraphy and whose only information comes from lithic arrowheads. This classifier is based on the Dirichlet-multinomial inferential process and posterior predictive distributions. The procedure is applied to predict the period of a set of undated sites located in the east of the Iberian Peninsula during the IVth and IIIrd millennium cal. BC., Comment: 12 pages, 5 figures, 3 tables

Son casi innumerables las reflexiones que se han hecho en el campo de la metodología de las ciencias sociales, sobre la dicotomía, real o inexistente, entre las perspectivas de análisis cuantitativo y cualitativo, mientras que han sido menos abundantes los trabajos teóricos, aunque van siendo más frecuentes los empíricos, que han tratado de compatibilizar y/o complementar ambas perspectivas. En muchos casos los trabajos cualitativos cubren solamente los primeros pasos de la investigación social, como fases previas o exploratorias de la misma, para prolongarla después con una metodología más cuantitativa y extensiva como la inherente a las encuestas, al análisis de datos, multivariados, causales u otros. Este artículo presenta un ejemplo concreto del planteamiento inverso. Se parte de una gran encuesta con el objetivo de estructurar la realidad objeto de estudio, para posteriormente aplicar técnicas cualitativas, concretamente grupos de discusión. Most times, the qualitative works have been the beginnings of research, or even more, they have shaped the exploratory stages before the dimensionalisation and categorisation for the use of extensive techniques as surveys. This article is an example of an inverse planning which starts from a large survey in order to structure the reality which is the target of the research and afterwards to apply qualitative techniques, concretely discussion groups. As a result, it is concerned with increasing the validity and representativeness of the discourses by individuals and groups which have been gained from the features obtained in the structural stage.

The thesis ended up providing a set of tools for assessing the variability differences between production batches of the same drug in pharmaceutical datasets. Its main feature is the Mahalanobis-Dispersion model which, based in the multivariate distance of Mahalanobis, computes various measures of data dispersion within each batch then, these measures can be statistically tested at a desired significance level. The chosen statistical test is the one-way analysis of variance (ANOVA), it can be performed as usual as well as using a iterative procedure which pops out of the dataset the batch with higher dispersion in each iteration, like what one would do with the Cochran's C test in univariate cases.

This paper proposes a generalized framework to analyze spatial count data under a unilateral regular lattice structure based on thinning type models. We start from the simple spatial integer-valued auto-regressive model of order 1. We extend this model in certain directions. First, we consider various distributions as choices for the innovation distribution to allow for additional overdispersion. Second, we allow for use of covariate information, leading to a non-stationary model. Finally, we derive and use other models related to this simple one by considering simplification on the existing model. Inference is based on conditional maximum likelihood approach. We provide simulation results under different scenarios to understand the behaviour of the conditional maximum likelihood. A real data application is also provided. Remarks on how the results extend to other families of models are also given.

The farthest-color Voronoi diagram (FCVD) is a farthest-site Voronoi structure defined on a family \(\mathcal {P} \) of m point-clusters in the plane, where the total number of points is n. The FCVD finds applications in problems related to color spanning objects and facility location. We identify structural properties of the FCVD, refine its combinatorial complexity bounds, and list conditions under which the diagram has O(n) complexity. We show that the diagram may have complexity \(\varOmega (n+m^2)\) even if clusters have disjoint convex hulls. We present construction algorithms with running times ranging from \(O(n\log n)\), when certain conditions are met, to \(O((n+s(\mathcal {P} ))\log ^3n)\) in general, where s(P) is a parameter reflecting the number of straddles between pairs of clusters in \(\mathcal {P} \) (\(s(P)\in O(mn)\)). A pair of points \(q_1,q_2\in Q\) is said to straddle \(p_1,p_2\in P\) if the line segment \(q_1q_2\) intersects (straddles) the line through \(p_1,p_2\) and the disks through \((p_1,p_2,q_1)\) and \((p_1,p_2,q_2)\) contain no points of P, Q. The complexity of the diagram is shown to be \(O(n+s(\mathcal {P} ))\).

A role of iron as a target to prevent stroke-induced neurodegeneration has been recently revisited due to new evidence showing that ferroptosis inhibitors are protective in experimental ischemic stroke and might be therapeutic in other neurodegenerative brain pathologies. Ferroptosis is a new form of programmed cell death attributed to an overwhelming lipidic peroxidation due to excessive free iron and reactive oxygen species (ROS). This study aims to evaluate the safety and tolerability and to explore the therapeutic efficacy of the iron chelator and antioxidant deferoxamine mesylate (DFO) in ischemic stroke patients. Administration of placebo or a single DFO bolus followed by a 72 h continuous infusion of three escalating doses was initiated during the tPA infusion, and the impact on blood transferrin iron was determined. Primary endpoint was safety and tolerability, and secondary endpoint was good clinical outcome (clinicalTrials.gov NCT00777140). DFO was found safe as adverse effects were not different between placebo and DFO arms. DFO (40–60 mg/Kg/day) reduced the iron saturation of blood transferrin. A trend to efficacy was observed in patients with moderate-severe ischemic stroke (NIHSS > 7) treated with DFO 40–60 mg/Kg/day. A good outcome was observed at day 90 in 31% of placebo vs. 50–58% of the 40–60 mg/Kg/day DFO-treated patients.

The goal of this project is to study the relationship between the genotype (SNPs) and phenotype (feed efficiency, FE) of a cow population of size n = 608. In order to achieve this goal, a descriptive analysis of the genetic data has been done as a first step. Data has also been filtered, by the deletion and imputation of missing data, as well as through a genetic exploration process, performing a minor allele frequency (MAF) histogram and the Hardy Weinberg Equilibrium (HWE) test. After the data has been cleaned, genetic relatedness research has been carried out through PCA and MDS, and by the study of the number of alleles shared by each pair of cattle individuals. Once all the above-mentioned analyses have been concluded, the GWAS has been conducted. Linear models (LM) and linear mixed models (LMM) have been created to study the relationship between cow feed efficiencies and genetic data. GWASs have also been performed for milk, protein and fat traits. One polymorphism has been found to be significantly associated with all these variables.

Measurement of external load in players provides objective information to optimise the weekly balance between training and recovery to improve performance and prevent injuries. Our aim was to evaluate the incidence of sports-related muscle injuries of the lower limb in relation to external load, measured by global positioning system (GPS), in football players. A descriptive study was carried out. Data were collected from 71 professional male football players (30 professionals and 41 youth players) from an elite football club competing in the Spanish and European League in the 2017–2018 season. As external load variables, we measured High Metabolic Load Distance (HMLD), High Speed Running (HSR), Player Load (PL), and Total Distance (TD) through GPS. Injury rate (IR) was calculated both in relation to such GPS load metrics and to load exposure time. We considered categories (youth and professional), playing positions (centre back, full back, midfielder, and forward), and training day with respect to match-day (-4MD, -3MD, -2MD, -1MD, MD, +1MD, +2MD). The GPS load metrics HMLD, HSR, PL, and TD showed very similar patterns across categories and positions, but varied according to training session or MD. The highest loads were observed on MD and three days prior to the match (-3MD). Similarly, the overall IR, both calculated per load exposure time and per GPS load metrics, was highest on MD and -3MD. Again, no differences were observed between youth and professional players. Midfielders demonstrated the highest IR in all metrics, followed by the forwards. In conclusion, this study suggests that external load and incidence of muscle injuries are directly proportional. Therefore, the measurement of more external load variables other than load exposure time, such as the GPS metrics HMLD, HSR, PL, and TD may help to describe the pattern and magnitude of injuries. Future studies based on ours may help to further improve the understanding of the incidence of muscle injuries on the basis of external loads measurements in different football teams. JC and MC acknowledge the financial support from Ministerio de Ciencia e Innovación y Ministerio de Universidades (Ref: PID2019-104830RB-I00 / AEI DOI: 10.13039/501100011033).

Treballs Finals de Grau en Estadística UB-UPC, Facultat d'Economia i Empresa (UB) i Facultat de Matemàtiques i Estadística (UPC), Curs: 2019-2020. Tutors: Ignasi Puig De Dou; Lourdes Rodero De Lamo, [cat] L’anàlisi de conglomerats és un mètode multivariant que té com a objectiu principal identificar grups d’objectes amb característiques similars dins d’una base de dades numèriques. Actualment però, aquesta branca de l’estadística està desenvolupant mètodes que permetin l’anàlisi de bases de dades mixtes, per tal de poder utilitzar tant les variables descriptives numèriques com les categòriques dels diversos objectes. Aquests criteris d’agrupació es poden classificar en dos grans grups: els mètodes jeràrquics i els no jeràrquics. En el següent treball es realitza una clusterització de les dades dels clients d’un majorista de ferreteria industrial a fi de poder-los agrupar en varis grups homogenis, mitjançant dos mètodes d’agrupació: el mètode de Ward i el Partition Around Medoids. A fi de poder crear aquests grups és necessari calcular un coeficient de similitud per tal de conèixer les distàncies entre els individus. Així doncs, s’utilitzarà el coeficient de Gower, ja que permet tractar amb dades numèriques i categòriques a la vegada. No obstant, també es realitzarà un anàlisi de sensibilitat d’aquesta mesura per tal de comprovar la seva robustesa., [eng] Traditionally, the cluster analysis has only been used in numerical data bases with the main objective being the identification of object groups with similar characteristics within those databases. This is an on-growing branch of statistics science that tries to incorporate methods that allow us to perform this mix database object categorization. In this project we will be carrying out a cluster analysis of an industrial hardware wholesaler’s client information. By doing so, we can recognize those that follow specific patterns of behaviour, which will allow us to perform a more accurate follow-up and segment the price cut campaigns according to their own interests. For us to accomplish this objective, we have opted for the use of both a hierarchical aggregation method and a non-hierarchical one: the Ward and the Partition Around Medoids methods, respectively. However, for us to be able to create these groups we need to calculate a similarity coefficient for us to know the distances between the various individuals. In this study, we have opted for the use of the Grower coefficient, seeing as it allows us to handle numerical and categorical data at the same time. Hereunder is the detailed explanation of the client characterisation process, starting with the creation of the database and following up with the description of the various groups used. A Grower distance sensibility analysis is also included for the validation of the solidness of this measure.

The purpose of this project is to create a predictive model capable of doing accurate predictions to measure the Delta Price. To do so, another indicator is needed, which is called Price Elasticity, that measures the change percentage of the demanded quantity over the change percentage price. In the view of calculating the Price Elasticity, two elements are required: price and quantity. For this study, three different approaches will be implemented and compared. The model prediciting the best will be used to calculate the future Price Elasticity, which then will be used to Recommend the Delta Price.

Treballs Finals de Grau en Estadística UB-UPC, Facultat d'Economia i Empresa (UB) i Facultat de Matemàtiques i Estadística (UPC), Curs: 2019-2020. Tutor: Mireia Besalú Mayol, [cat] La necessitat d’identificar característiques que ens permetin diferenciar dos o més grups cada cop és més freqüent. L’anàlisi discriminant consisteix a analitzar aquestes característiques que ajuden a la classificació de dos o més grups. L’objectiu d’aquest estudi és arribar a una bona classificació d’una base de dades que té com a observacions les reserves d’un complex hoteler diferenciat en tres seccions diferents. Aquestes tres seccions de l’hotel seran els nostres grups de la variable dependent. Els mètodes de classificació amb els quals volem aconseguir una assignació correcta de reserves noves són l’anàlisi discriminant lineal, l’anàlisi discriminant quadràtic i l’anàlisi k-nn veïns més pròxims. Seguidament validarem aquests resultats estudiant la capacitat predictiva. D’aquesta forma arribarem a veure amb quin mètode obtenim la taxa d’encerts de classificació més elevada., [eng] The necessity of identifying characteristics that allow us to distinguish two or more groups of individuals is becoming more common. The discriminant analysis consists in analyze these characteristics that you can use at the time of classifying in two or more groups. The goal of this study is to get a good classification of a data base that contain observations of reservations of a hotel complex differentiated in three different sections. These sections will be the groups of our dependent variable. The classification methods with which we want to achieve the classification are linear discriminant analysis, quadratic discriminant analysis and k-nn nearest neighboring analysis. Then, we will validate these results by studying the predictive ability of those methods. In this way, we will be able to see with which method we obtain the highest success rate of classification.

Treballs Finals de Grau en Estadística UB-UPC, Facultat d'Economia i Empresa (UB) i Facultat de Matemàtiques i Estadística (UPC), Curs: 2019-2020. Tutor: Ferran Reverter Comes, [cat] Aquest treball pretén estudiar quines són les estratègies seguides pels nostres oponents en un nivell NL5 i NL10 del joc Texas Hold’em Poker per tal de poder aplicar el nostre joc adaptat a cada jugador. Per dur a terme la classificació de possibles estratègies es realitzarà un estudi específic prèvi per aquelles variables més significatives i es descriuran aquells rangs de percentatges guanyadors i perdedors. Posteriorment farem un estudi de possibles agrupacions de clústers entre jugadors per més tard, intentar predir un model que ens classifiqui aquests mitjançant arbres de classificació., [eng] This work aims to study the strategies followed by our opponents at an NL5 and NL10 level of Texas Hold’em Poker game in order to be able to apply our game adapted to each player. To carry out the classification of possible strategies, a specific prior study will be carried out for those most significant variables and those ranges of winning and losing percentages will be described. Later we will make a study of possible groupings of clusters between players for later, to try to predict a model that classifies these to us by means of classification trees

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Classical Pre-Post Intervention Studies are often analyzed using traditional statistics. Nevertheless, the nutritional interventions have small effects on the metabolism and traditional statistics are not enough to detect these subtle nutrient effects. Generally, this kind of studies assumes that the participants are adhered to the assigned dietary intervention and directly analyzes its effects over the target parameters. Thus, the evaluation of adherence is generally omitted. Although, sometimes, participants do not effectively adhere to the assigned dietary guidelines. For this reason, the Trajectory Map is proposed as a visual tool where dietary patterns of individuals can be followed during the intervention and can also be related with nutritional prescriptions. The Trajectory Analysis is also proposed allowing both analysis: 1) adherence to the intervention and 2) intervention effects. The analysis is made by projecting the differences of the target parameters over the resulting trajectories between states of different time-stamps which might be considered either individually or by groups. The proposal has been applied over a real nutritional study showing that some individuals adhere better than others and some individuals of the control group modify their habits during the intervention. In addition, the intervention effects are different depending on the type of individuals, even some subgroups have opposite response to the same intervention.

This article considers the combination of two binary or two time-to-event endpoints to form the primary composite endpoint for leading a trial. • It discusses the relative efficiency of choosing a composite endpoint over one of its components in terms of: the frequencies of observing each component; the relative treatment effect of the tested therapy; and the association between both components. • We highlight the very important role of the association between components in choosing the most efficient endpoint to use as primary. • For better grounded future trials, we recommend trialists to always reporting the association between components of the composite endpoint • Common fallacies to note when using composite endpoints: i) composite endpoints always imply higher power; ii) treatment effect on the composite endpoint is similar to the average effects of its components; and iii) the probability of observing the primary endpoint increases significantly.

Running training plans have become a trend between professional and amateur runners to train for races. However, not all of them have a preliminary study to guarantee their effectivity. In this project, we analyse running sessions using the Principal Component Analysis method in order to find which are those parameters that a running training plan needs to focus on and how they are trained to help the runner to be faster. As an application, we analyse a particular training plan with the information obtained from the method and we find out how its indicator qualify every run, compared with the planned one.

Archetypal analysis is an exploratory tool that explains a set of observations as mixtures of pure (extreme) patterns. If the patterns are actual observations of the sample, we refer to them as archetypoids. For the first time, we propose to use archetypoid analysis for binary observations. This tool can contribute to the understanding of a binary data set, as in the multivariate case. We illustrate the advantages of the proposed methodology in a simulation study and two applications, one exploring objects (rows) and the other exploring items (columns). One is related to determining student skill set profiles and the other to describing item response functions.

The final publication is available at Springer via http://dx.doi.org/10.1007/s40857-020-00188-0 A Statistical Energy Analysis (SEA) model of a 510 liter capacity refrigerator is used to calculate the Sound Pressure Level (SPL) generated in a reverberation room. This is a reliable indicator of the vibroacoustic performance of the refrigerator and it is in some countries as a pre-commercialization test. The main contributions of the SEA model are, the characterization of the refrigerator structure (three-layer: HIPS, polyurethane foam and steel), and the modeling of important components such as internal chambers or ventilation gratings. The simulation results are successfully compared with laboratory measurements. The SEA model is then used to understand the vibroacoustic behavior of the refrigerator and to establish the most critical transmission paths and radiation mechanisms.

In this project we investigate the application of multi-omics clustering methods for disease subtyping using a real dataset of patients with Inflammatory Bowel Disease (IBD). IBD is a prevalent, heterogeneous, autoimmune disease, and its molecular characterization is expected to yield biological knowledge that might translate eventually into the clinical practice. The main contribution of this project is that with the currently proposed strategies, the methods do not recover the known clinical subtypes. However, some clinical and biological insights could still be extracted using the current methodology.

Treballs Finals de Grau en Estadística UB-UPC, Facultat d'Economia i Empresa (UB) i Facultat de Matemàtiques i Estadística (UPC), Curs: 2019-2020 Tutors: Ferran Reverter; Esteban Vegas, [eng] Nowadays, high dimensional data is ubiquitous: you can think for example in images, videos or texts. Unfortunately, this property can harm seriously the performance of some algorithms. In this project, I analyse how dimensionality reduction can help clustering improve its performance. In order to do that, I distinguish three di erent clustering strategies: Traditional, two-stages and deep clustering. In the rst one, the clustering is applied to the raw data while in the other two it is applied to a low dimensional representation. I focus especially on the latter approach, which has shown promising performance in the last years. The di erences between these approaches are illustrated doing a series of experiments and visualisations and comparing the results., [cat] Actualment, les dades d'alta dimensi o s on omnipresents: es pot pensar, per exemple, en imatges, v deos o textos. Malauradament, aquesta propietat pot perjudicar greument el rendiment d'alguns algorismes. En aquest projecte, analitzo com la reducci o de dimensionalitat pot ajudar a que el clustering millori el seu rendiment. Per fer-ho, distingeixo tres estrat egies de clusteritzaci o diferents: la tradicional, la de dues etapes i el Deep clustering. En la primera, l'agrupaci o s'aplica a les dades brutes mentre que en les altres dos s'aplica a una representaci o de baixa dimensi o. En el treball em centro especialment en aquest ultim enfocament, que ha demostrat un rendiment prometedor en els darrers anys. Les difer encies entre aquestes estrat egies s'il·lustren fent una s erie d'experiments i visualitzacions i comparant els resultats.

Treballs Finals del Grau d'Economia i Estadística. Doble titulació interuniversitària, Universitat de Barcelona i Universitat Politècnica de Catalunya. Curs: 2019-2020. Tutors: Montserrat Guillén; Mònica Serrano, [cat] Són evidents les transformacions i la rellevància del consum en el segle XXI, on el nivell de benestar, qualitat de vida i classe social estan fortament lligades a la capacitat d’adquisició. A més, la globalització, el consum de masses i la identitat personal enforteix la importància de conèixer i estudiar els nous patrons de consum, tant a nivell econòmic com sociodemogràfic. De la mateixa manera, també resulta essencial analitzar la diversitat en la configuració de les llars per entendre millor els patrons de consum per un millor disseny de les polítiques econòmiques i socials. Per aquesta raó, sorgeix la necessitat d’interpretar les estadístiques oficials des de una altra perspectiva. L’objectiu d’aquest projecte és conèixer quins factors determinen els patrons de consum en funció de la configuració de les llars d’Espanya per l’any 2018., [eng] There is evidence that consumption in the 21st century has transformed and has gained new relevance when relating the level of wellbeing, quality of life and social class to acquisition power. In addition, globalization, mass consumption and personal identity reinforces the importance of studying and analyzing new patterns of consumption, both economically and socio - demographically. Similarly, it is also essential to analyze the diversity of household composition to better understand the consumer spending to design and apply better economic policies. For this reason, there is a need to interpret official statistics from another perspective. The aim of this project is to study which factors determine the consumption patterns by the different types of household composition in Spain.

Mixed Poisson models are most relevant to the analysis oflongitudinal count data in various disciplines. A conventional specification of such modelsrelies on the normality of unobserved heterogeneity effects. In practice, such an assumptionmay be invalid, and non-normal cases are appealing. In this paper, we propose a modellingstrategy by allowing the vector of effects to follow the multivariate skew-normal distribution.It can produce dependence between the correlated longitudinal counts by imposing severalstructures of mixing priors. In a Bayesian setting, the estimation process proceeds by samplingvariants from the posterior distributions. We highlight the usefulness of our approach byconducting a simulation study and analysing two real-life data sets taken from the GermanSocioeconomic Panel and the US Centers for Disease Control and Prevention. By a comparativestudy, we indicate that the new approach can produce more reliable results compared totraditional mixed models to fit correlated count data.

Treballs Finals del Grau d'Economia i Estadística. Doble titulació interuniversitària, Universitat de Barcelona i Universitat Politècnica de Catalunya. Curs: 2019-2020. Tutor: Josep M. Oller Sala, [cast] Este trabajo ha tenido como objetivo establecer y estudiar una metodología de comparación de conjuntos de datos que representan a los mismos individuos pero en diferentes tiempos. Se han estudiado datos electorales donde se ha realizado un análisis exploratorio inicial para conocer los datos y su contexto utilizando algunos métodos estadísticos como ACP y clustering. Se ha determinado una metodología de comparación basada en reducir la dimensión de los datos a dos y el traslado de los conjuntos de datos al mismo espacio, observando entonces su desplazamiento mediante la visualización gráfica, y su correlación con las variables, visualizadas estas también en el mapa junto a los conjuntos de datos. Los resultados de este estudio muestran las tendencias de los individuos en el tiempo según las variables, y la metodología puede ser ampliable tanto a más datos electorales como a conjuntos de datos de otra naturaleza., [eng] This project’s aim was to define and study a methodology that allows to compare different datasets which represent the same individuals but on different spots in time. We have chosen to study electoral data. A first research analysis has been done in order to get to know the features of the data and its context. In this initial analysis, some statistical tools have been used such as PCA and clustering. The comparison methodology stablished in this project is based on, first, the dimensionality reduction of the datasets and the shift of these datasets to the same space. After, the individuals’ changes from the pass of the time could be seen. If this movement is compared with the placement of variables on the same space, we can know the relationship with the groups of individuals and the variables, in order to get some ideas on why they move. The methodology stablished can be expanded not only to more electoral data, but also to other datasets with a different nature.

It is well known that outliers can affect both the estimation of parameters and volatilities when fitting a univariate GARCH-type model. Similar biases and impacts are expected to be found on correlation dynamics in the context of multivariate time series. We study the impact of outliers on the estimation of correlations when fitting multivariate GARCH models and propose a general detection algorithm based on wavelets, that can be applied to a large class of multivariate volatility models. Its effectiveness is evaluated through a Monte Carlo study before it is applied to real data. The method is both effective and reliable, since it detects very few false outliers. info:eu-repo/semantics/publishedVersion

© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ Here we present new and original data on the endogenous con-version of tyrosol (Tyr) into hydroxytyrosol (OHTyr) in humans and its effects on the cardiovascular system. A randomized,crossover, controlled clinical trial was performed with individuals at cardiovascular risk (n¼33). They received white wine (WW)(females 1, males 2 standard drinks/day), WW plus Tyr capsules(WWþTyr) (25mg Tyr capsule, one per WW drink), and water(control)ad libitum. Intervention periods were of 4 weeks pre-ceded by three-week wash-out periods. We assessed the conversion of Tyr to OHTyr, its interaction with a polygenic activity score(PAS) fromCYP2A6andCYP2D6genotypes, and the effects on cardiovascular risk markers. For further details and experimental findings please refer to the article“Cardiovascular benefits of tyrosol and its endogenous conversion into hydroxytyrosol Inhumans. A randomized, controlled trial”

In this master's thesis we combine techniques from the field of Compositional Data Analysis (CoDA) with mixed-effects models to study the relationship between dairy calves' growth and their amino acid compositon in blood, being the detection of potentially growth-limiting amino acids that can provoke changes in urea levels of particular interest.

DECA

© 2016 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ Traumatic brain injury (TBI) is the leading cause of death and disability in children and young adults worldwide. Cognitive rehabilitation (CR) plans consist of a sequence of CR tasks targeting main cognitive functions. There is not enough on-field experience yet regarding which specific intervention (tasks or exercise assignment) is more appropriate to help therapists to design plans with significant effectiveness on patient improvement. The selection of specific tasks to be prescribed to the patient and the order in which they might be executed is currently decided by the therapists based on their experience. In this paper a new data mining methodology is proposed, combining several tools from Artificial Intelligence, clustering and post-processing analysis to identify regularities in the sequences of tasks in such a way that treatment profiles (classes) can be discovered. Due to the cumulative effect of rehabilitation tasks, small variations within the sequence of tasks performed by the patient do not significantly change the final outcomes in rehabilitation and makes it difficult to find discriminant rules by using the traditional machine learning inductive methods. However, by relaxing the formalization of the problem to find patterns that might include small variations, and introducing motif discovery techniques in the proposed methodology, the complexity of the neurorehabilitation phenomenon can be better captured and a global structure of successful treatment task sequences can be devised. Following this, the relationship between the discovered patterns and the CR treatment response are analyzed, offering a richer perspective than that provided by the single task focus traditionally used in the CR field. The paper provides a definition of the whole methodological approach proposed from a formal point of view, and its application to a real dataset. Comparisons with traditional AI approaches are also presented and the contribution of the proposed methodology to the AI field discussed.

In this paper we propose a Bayesian hierarchical spatio-temporal model for the joint analysis of multiple diseases which includes specific and shared spatial and temporal effects. Dependence on shared terms is controlled by disease-specific weights so that their posterior distribution can be used to identify diseases with similar spatial and temporal patterns. The model proposed here has been used to study three different causes of death (oral cavity, esophagus and stomach cancer) in Spain at the province level. Shared and specific spatial and temporal effects have been estimated and mapped in order to study similarities and differences among these causes. Furthermore, estimates using Markov chain Monte Carlo and the integrated nested Laplace approximation are compared.

Some studies have reported that the outcome of the patients with HPV positive and p16 negative is significantly worse than the outcomes of patients who are HPV positive and p16 positive. The aim of this study is to increase the number of the cases HPV-negative/p16-positive since most cohorts had amassed relatively few cases, and to clearly define the prognosis of these patients and describe their characteristics in terms of age, gender, TNM stage and other variables. To this end, chi-square test of independence and multinomial logistic regressions with random effects were used.

We propose a class of goodness–of–fit test procedures for arbitrary parametric families of circular distributions with unknown parameters. The tests make use of the specific form of the characteristic function of the family being tested, and are shown to be consistent. We derive the asymptotic null distribution and suggest that the new method be implemented using a bootstrap resampling technique that approximates this distribution consistently. As an illustration, we then specialize this method to testing whether a given data set is from the von Mises distribution, a model that is commonly used and for which considerable theory has been developed. An extensive Monte Carlo study is carried out to compare the new tests with other existing omnibus tests for this model. An application involving five real data sets is provided in order to illustrate the new procedure.

El objetivo de este estudio fue describir la localización anatómica, el tipo y la gravedad de las lesiones ocurridas en las competiciones oficiales de las ligas de verano masculinas (2005-2015) de Lucha Leonesa. Al objeto de realizar un análisis más profundo, se tuvo en cuenta, el perfil del luchador. Se produjeron un total de 401 lesiones, siendo los luchadores de mayor nivel los que menos número de lesiones sufrieron. Las contusiones fueron las lesiones más frecuentes (41,4%) pero entre las graves fueron los esguinces (42,3%). Los miembros inferiores fueron la localización más frecuente en el total de lesiones (38,2%) y entre las graves (45,4%). La rodilla, el tórax y el hombro acumulaban casi la mitad de las lesiones y el 68% de las lesiones graves. Los resultados obtenidos sugieren que las características de las lesiones que se pueden observar en la lucha leonesa son similares a las de otros deportes de combate, This study aimed at describing the anatomical location, type and severity of the sport injuries observed during the Leonese Wrestling “masculine summer leagues” official tournaments (2005-2015). The wrestler’s profile was taken into account as a way to achieve a deep analysis. A total of 401 injuries were registered. The best wrestlers were the ones that suffered the least number of injuries. Contusions were the type of injuries more frequently registered (41.4%), while sprains were the most common cause of severe injury (42.3%). The most frequent anatomical location affected and the one in which a great number of severe injuries occurred were the lower limbs (38.2% and 45.4%). The knee, thorax, and shoulder accounted for almost half of the total injuries and represented 68% of the severe injuries. The obtained results suggest that Leonese Wrestling show a similar injury pattern to the one observed in other combat sports, Este trabajo contó con financiación parcial de la Diputación de León, la Federación Territorial de Castilla y León de Lucha y CIBERESP (MTM2015-64465-C2-1-R; MINECO / FEDER)

OBJECTIVE: To evaluate the effect of effervescent paracetamol on office and ambulatory blood pressure (BP) compared with noneffervescent paracetamol in hypertensive patients. DESIGN: This was a multicenter open crossover randomized clinical trial. SETTING: Primary care centers in Catalonia and the Basque Country. PARTICIPANTS: Inclusion criteria were office BP 150/95¿mmHg or less and daytime ambulatory BP 140/90¿mmHg or less, stable pharmacologic or nonpharmacologic antihypertensive treatment, and concomitant chronic osteoarticular pain. INTERVENTIONS: Baseline randomized assignment to 3-week periods of effervescent paracetamol (1¿g three times a day) first and noneffervescent paracetamol later, or inversely, during a 7-week study period. At the start and end of each treatment period, 24-h ambulatory BP monitoring was performed. MAIN OUTCOME MEASURES: Differences in 24-h SBP between baseline and end of both treatment periods. The main analyses were performed according to the intention-to-treat principle. RESULTS: In intention-to-treat analysis, 46 patients were analyzed, 21 were treated with paracetamol effervescent and noneffervescent later, and 25 followed the opposite sequence. The difference in 24-h SBP between the two treatments was 3.99¿mmHg (95% confidence interval 1.35-6.63; P¿=¿0.004), higher in the effervescent paracetamol treatment period. Similarly, the per-protocol analysis showed a difference in 24-h SBP between the two groups of 5.04¿mmHg (95% confidence interval 1.80-8.28; P¿=¿0.004), higher in the effervescent paracetamol treatment period. Self-reported pain levels did not differ between groups and did not vary by treatment period. No serious adverse events were reported in either study arm. CONCLUSION: Effervescent paracetamol tablets are responsible for a significant daytime and overall increase in ambulatory 24-h SBP. TRIAL REGISTRATION: NCT: 02514538 EudraCT: 2010-023485-53.

Forma parte de un trabajo de investigación con una entidad gubernamental de México que es objeto de confidencialidad.

Nearest neighbour methods are employed for drawing inferences about spatial patterns of points from two or more classes. We introduce a new pattern called correspondence which is motivated by (spatial) niche/habitat specificity and segregation, and define an associated contingency table called a correspondence contingency table, and examine the relation of correspondence with the motivating patterns (namely, segregation and niche specificity). We propose tests based on the correspondence contingency table for testing self and mixed correspondence and determine the appropriate null hypotheses and the underlying conditions appropriate for these tests. We compare finite sample performance of the tests in terms of empirical size and power by extensive Monte Carlo simulations and illustrate the methods on two artificial data sets and one real-life ecological data set.