Semiparametric finite mixture of regression models with Bayesian P-splines

Mixture models provide a useful tool to account for unobserved heterogeneity and are at the basis of many model-based clustering methods. To gain additional flexibility, some model parameters can be expressed as functions of concomitant covariates. In this Paper, a semiparametric finite mixture of regression models is defined, with concomitant information assumed to influence both the component weights and the conditional means. In particular, linear predictors are replaced with smooth functions of the covariate considered by resorting to cubic splines. An estimation procedure within the Bayesian paradigm is suggested, where smoothness of the covariate effects is controlled by suitable choices for the prior distributions of the spline coefficients. A data augmentation scheme based on difference random utility models is exploited to describe the mixture weights as functions of the covariate. The performance of the proposed methodology is investigated via simulation experiments and two real-world datasets, one about baseball salaries and the other concerning nitrogen oxide in engine exhaust

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device’s walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson’s Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and − 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application. Trial registration: ISRCTN – 12246987

Understanding dependency patterns in structural and functional brain connectivity through fMRI and DTI Data

Neuroscience and neuroimaging have been providing new challenges for statisticians and quantitative researchers in general. As datasets of increasing complexity and dimension become available, the need for statistical techniques to analyze brain related phenomena becomes prominent. In this paper, we delve into data coming from functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI). The aim is to combine information from both sources in order to learn possible patterns of dependencies among regions of interest (ROIs) of the brain. First, we infer positions of these regions in a latent space, using the observed structural connectivity provided by the DTI data, to understand if physical spatial coordinates suitably reflect how ROIs are effectively interconnected. Secondly, we inspect Granger causality in the fMRI data in order to capture patterns of activations between ROIs. Then, we compare results from the analysis on these datasets, to find a link between functional and structural connectivity. Preliminary findings show that latent space positions well reflect hemisphere separation of the brain but are not perfectly connected to all the other structural partitions (that is, lobe, cortex, etc.); furthermore, activations of ROIs inferred from fMRI data are tied to observed structural connections derived from DTI scans

Bayesian smooth-and-match inference for ordinary differential equations models linear in the parameters

Dynamic processes are crucial in many empirical fields, such as in oceanography, climate science, and engineering. Processes that evolve through time are often well described by systems of ordinary differential equations (ODEs). Fitting ODEs to data has long been a bottleneck because the analytical solution of general systems of ODEs is often not explicitly available. We focus on a class of inference techniques that uses smoothing to avoid direct integration. In particular, we develop a Bayesian smooth-and-match strategy that approximates the ODE solution while performing Bayesian inference on the model parameters. We incorporate in the strategy two main sources of uncertainty: the noise level of the measured observations and the model approximation error. We assess the performance of the proposed approach in an extensive simulation study and on a canonical data set of neuronal electrical activity

Identifying overlapping terrorist cells from the noordin top actor\u2013event network

Actor\u2013event data are common in sociological settings, whereby one reg-isters the pattern of attendance of a group of social actors to a number of events. We focus on 79 members of the Noordin Top terrorist network, who were monitored attending 45 events. The attendance or nonattendance of the terrorist to events defines the social fabric, such as group coherence and social communities. The aim of the analysis of such data is to learn about the affiliation structure. Actor\u2013event data is often transformed to actor\u2013actor data in order to be further analysed by network models, such as stochastic block models. This transformation and such analyses lead to a natural loss of infor-mation, particularly when one is interested in identifying, possibly overlap-ping, subgroups or communities of actors on the basis of their attendances to events. In this paper we propose an actor\u2013event model for overlapping communities of terrorists which simplifies interpretation of the network. We propose a mixture model with overlapping clusters for the analysis of the binary actor\u2013event network data, called manet, and develop a Bayesian procedure for inference. After a simulation study, we show how this analysis of the terrorist network has clear interpretative advantages over the more traditional approaches of affiliation network analysis

A five-year cohort study on testicular tumors from a population-based canine cancer registry in central Italy (Umbria)

Canine testicular tumors account for about 90 % of tumors affecting the male genitalia. Seminomas (SEM), Sertoli cell tumors (SCT), and interstitial cell tumors (ICT) are the most common histological diagnoses, but their incidence shows high variability among studies. Our aim is to report the results on the analysis of testicular tumors recorded by the population-based Umbria Canine Cancer Registry (CCR) for a 5-year time period and to assess the value of tumor major diameter, measured during trimming, in discriminating neoplastic from non-neoplastic lesions. The study was conducted on 388 testicular tumors (on 1969 total male tumors) diagnosed in 355 dogs from 2014 to 2018. The median incidence was 35 cases/100,000 dogs, with a proportion equal to 19,7 % of all tumors. The incidence on neutered male dogs was 352/100,000. Most tumors were ICTs (50 %), with fewer SEMs and SCTs (29 % and 17 %, respectively). Only 3 % of tumors were mixed germ cell-sex cord-stromal tumors (MGC-SCST). Ten percent of cases had multiple tumors in the same testicle, with SEM-ICT being prevalent (69.2 %). Tumors in cryptorchid testes were 5.9 % of the total, mostly SCT (60.9 %). Mean age at diagnosis was 10.7 ± 2.7, with similar values for different tumor types. Significant incidence ratios (IRR) were found in Golden retriever (IRR 7.18, CI95 % 4.72–10.92) and in English cocker spaniel (IRR 2.30, CI95 % 1.28–4.13) when compared with mixed breed dogs. A value of 0.3 cm (major diameter) of lesions at the moment of trimming was helpful in discriminating a final tumor histological diagnosis from a non-tumor lesion. Since the number of tumors included in this evaluation was limited, further studies to confirm the diagnostic value of this measure are recommended. Our results provided information on the incidence of canine testicular tumors in Umbria region that can be compared with future results and incidence from other geographical areas if provided with reliable data on the total population, can offer solid information on the incidence and proportion of different tumor types in specific territories, contributing also to the supervision of its inhabitants’ health. Moreover, pathological data such as the major diameter of tumors can be obtained and contribute to diagnostic routine and standardization

Real-world walking cadence in people with COPD

Introduction The clinical validity of real-world walking cadence in people with COPD is unsettled. Our objective was to assess the levels, variability and association with clinically relevant COPD characteristics and outcomes of real-world walking cadence. Methods We assessed walking cadence (steps per minute during walking bouts longer than 10 s) from 7days’ accelerometer data in 593 individuals with COPD from five European countries, and clinical and functional characteristics from validated questionnaires and standardised tests. Severe exacerbations during a 12-month follow-up were recorded from patient reports and medical registries. Results Participants were mostly male (80%) and had mean±SD age of 68±8 years, post-bronchodilator forced expiratory volume in 1 s (FEV1) of 57±19% predicted and walked 6880±3926 steps·day−1. Mean walking cadence was 88±9 steps·min−1, followed a normal distribution and was highly stable within-person (intraclass correlation coefficient 0.92, 95% CI 0.90–0.93). After adjusting for age, sex, height and number of walking bouts in fractional polynomial or linear regressions, walking cadence was positively associated with FEV1, 6-min walk distance, physical activity (steps·day−1, time in moderate-to-vigorous physical activity, vector magnitude units, walking time, intensity during locomotion), physical activity experience and health-related quality of life and negatively associated with breathlessness and depression (all p<0.05). These associations remained after further adjustment for daily steps. In negative binomial regression adjusted for multiple confounders, walking cadence related to lower number of severe exacerbations during follow-up (incidence rate ratio 0.94 per step·min−1, 95% CI 0.91–0.99, p=0.009). Conclusions Higher real-world walking cadence is associated with better COPD status and lower severe exacerbations risk, which makes it attractive as a future prognostic marker and clinical outcome