Passes are undoubtedly the more frequent events in football and other team sports. Passing networks and their structural features can be useful to evaluate the style of play in terms of passing behavior, analyzing and quantifying interactions among players. The present paper aims to show how information retrieved from passing networks can have a relevant impact on predicting the match outcome. In particular, we focus on modeling both the scored goals by two competing teams and the goal difference between them. With this purpose, we fit these outcomes using Bayesian hierarchical models, including both in-match and network-based covariates to cover many aspects of the offensive actions on the pitch. Furthermore, we review and compare different approaches to include covariates in modeling football outcomes. The presented methodology is applied to a real dataset containing information on 125 matches of the 2016–2017 UEFA Champions League, involving 32 among the best European teams. From our results, shots on target, corners, and such passing network indicators are the main determinants of the considered football outcomes.

Introduction and Objectives Interstitial lung diseases (ILDs) encompass a heterogeneous group of parenchymal lung disorders which have a significant burden on quality of life and exercise. The primary purpose of this randomised pilot trial performed in advanced ILD was to determine the feasibility and efficacy of a multidisciplinary palliative care approach (including physiotherapist, psychologist, pulmonologists, and palliative care doctors) to relieve patients’ symptoms of dyspnoea, depression measured with the Center for Epidemiological Studies-Depression (CES-D) scale and quality-of-life (QoL) at 6 and 12 months. Matherials and Methods Fifty patients with confirmed interstitial lung disease at computed tomography (CT) scan and advanced disease were enrolled at our clinic. Patients were randomised to usual care group vs intervention group; in the intervention group, patients were scheduled to meet a physiotherapist, a psychologist, a palliative care doctor, and a pulmonologist specialized in ILD care. Data on dyspnoea, cough, quality of life and depression were recorded; patients in the intervention group were also tested to assess lower body flexibility and strength. Results: Both groups showed a worsening in dyspnoea during the time course of the trial, but the Borg scale was less in the intervention group at 6 and 12 months. A similar trend was observed also for the CES-D scale. No differences were observed for the other scales. Conclusions: A multi-disciplinary palliative care intervention in patients with advanced fibrosing interstitial lung disease is feasible and effective. Trial registration NCT02929966 on ClinGovTrial.

The analysis of variance, and mixed models in general, are popular tools for analyzing experimental data in psychology. Bayesian inference for these models is gaining popularity as it allows to easily handle complex experimental designs and data dependence structures. When working on the log of the response variable, the use of standard priors for the variance parameters can create inferential problems and namely the non-existence of posterior moments of parameters and predictive distributions in the original scale of the data. The use of the generalized inverse Gaussian distributions with a careful choice of the hyper-parameters is proposed as a general purpose option for priors on variance parameters. Theoretical and simulations results motivate the proposal. A software package that implements the analysis is also discussed. As the log-transformation of the response variable is often applied when modelling response times, an empirical data analysis in this field is reported.

The tipsae package implements a set of small area estimation tools for mapping proportions and indicators defined on the unit interval. It provides for small area models defined at area level, including the classical beta regression, zero- and/or one-inflated beta and flexible beta ones, possibly accounting for spatial and/or temporal dependency structures. The models, developed within a Bayesian framework, are estimated through Stan language, allowing fast estimation and customized parallel computing. The additional features of the tipsae package, such as diagnostics, visualization and exporting functions as well as variance smoothing and benchmarking functions, improve the user experience through the entire process of estimation, validation and outcome presentation. A shiny application with a user-friendly interface further eases the implementation of Bayesian models for small area analysis.

Cutaneous melanoma is one of the most lethal forms of skin cancer, and its incidence is increasing globally. Its diagnosis typically relies on manual histopathological examination, a process that is both complex and time consuming. In this study, we propose an automated diagnostic tool, capable of generating interpretable results to aid clinical decision-making. A total of 146 whole slide images are included in the study, encompassing various lesion types: congenital nevi, dysplastic nevi, melanomas, and melanomas on nevi. The images were first processed using a multi-resolution image processing pipeline with the aim of segmenting nuclei, evaluating their geometrical and morphological features, as well as their spatial organization. To characterize each slide, these features were synthesized into 44 variables, which were then subjected to Linear Discriminant Analysis. Through this procedure, 18 relevant variables were identified demonstrating good performance in melanoma detection, as validated through Monte Carlo Cross-Validation. These variables were also interpreted within the framework of established histopathological diagnostic insights. By refining the analysis to the cellular level, we emulated standard clinical evaluation practices, ensuring that every aspect of the diagnostic process was accessible and verifiable by medical professionals. The proposed tool can offers significant potential to support clinicians in various tasks, such as prioritizing the analysis of critical samples and providing a secondary diagnostic opinion in complex cases.

The paper targets the estimation of a poverty rate at the upazila level in Bangladesh through the use of demographic and health survey (DHS) data. Upazilas are administrative regions equivalent to counties or boroughs whose sample sizes are not large enough to provide reliable estimates or are even absent. We tackle this issue by proposing a small-area estimation model complementing survey data with remote sensing information at the area level. We specify an extended Beta mixed regression model within the Bayesian framework, allowing it to accommodate the peculiarities of sample data and to predict out-of-sample rates. Specifically, it enables to include estimates equal to either 0 or 1 and to model the strong intra-cluster correlation. We aim at proposing a method that can be implemented by statistical offices as a routine. In this spirit we consider a regularizing prior for coefficients, rather than a model selection approach, to deal with a large number of auxiliary variables. We compare our methods with existing alternatives using a design-based simulation exercise and illustrate its potential with the motivating application.