A dimensionally reduced finite mixture model for multilevel data

AbstractRecently, different mixture models have been proposed for multilevel data, generally requiring the local independence assumption. In this work, this assumption is relaxed by allowing each mixture component at the lower level of the hierarchical structure to be modeled according to a multivariate Gaussian distribution with a non-diagonal covariance matrix. For high-dimensional problems, this solution can lead to highly parameterized models. In this proposal, the trade-off between model parsimony and flexibility is governed by assuming a latent factor generative model

Detecting relevant gene structure through independent component analysis Individuazione della struttura genica rilevante attraverso l'analisi in componenti indipendenti

Riassunto: Uno degli aspetti di maggior rilevanza nell'impiego di dati di espressione genica in problemi di classificazioneè rappresentato dalla necessità di procedere preliminarmente ad una selezione dei predittori. In questo lavoro sono approfonditi alcuni aspetti della procedura proposta i

COVID-19 in children and adolescents in Europe: a multinational, multicentre cohort study

Background To date, few data on paediatric COVID-19 have been published, and most reports originate from China. This study aimed to capture key data on children and adolescents with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection across Europe to inform physicians and health-care service planning during the ongoing pandemic. Methods This multicentre cohort study involved 82 participating health-care institutions across 25 European countries, using a well established research network—the Paediatric Tuberculosis Network European Trials Group (ptbnet)—that mainly comprises paediatric infectious diseases specialists and paediatric pulmonologists. We included all individuals aged 18 years or younger with confirmed SARS-CoV-2 infection, detected at any anatomical site by RT-PCR, between April 1 and April 24, 2020, during the initial peak of the European COVID-19 pandemic. We explored factors associated with need for intensive care unit (ICU) admission and initiation of drug treatment for COVID-19 using univariable analysis, and applied multivariable logistic regression with backwards stepwise analysis to further explore those factors significantly associated with ICU admission. Findings 582 individuals with PCR-confirmed SARS-CoV-2 infection were included, with a median age of 5·0 years (IQR 0·5–12·0) and a sex ratio of 1·15 males per female. 145 (25%) had pre-existing medical conditions. 363 (62%) individuals were admitted to hospital. 48 (8%) individuals required ICU admission, 25 (4%) mechanical ventilation (median duration 7 days, IQR 2–11, range 1–34), 19 (3%) inotropic support, and one (<1%) extracorporeal membrane oxygenation. Significant risk factors for requiring ICU admission in multivariable analyses were being younger than 1 month (odds ratio 5·06, 95% CI 1·72–14·87; p=0·0035), male sex (2·12, 1·06–4·21; p=0·033), pre-existing medical conditions (3·27, 1·67–6·42; p=0·0015), and presence of lower respiratory tract infection signs or symptoms at presentation (10·46, 5·16–21·23; p<0·0001). The most frequently used drug with antiviral activity was hydroxychloroquine (40 [7%] patients), followed by remdesivir (17 [3%] patients), lopinavir–ritonavir (six [1%] patients), and oseltamivir (three [1%] patients). Immunomodulatory medication used included corticosteroids (22 [4%] patients), intravenous immunoglobulin (seven [1%] patients), tocilizumab (four [1%] patients), anakinra (three [1%] patients), and siltuximab (one [<1%] patient). Four children died (case-fatality rate 0·69%, 95% CI 0·20–1·82); at study end, the remaining 578 were alive and only 25 (4%) were still symptomatic or requiring respiratory support. Interpretation COVID-19 is generally a mild disease in children, including infants. However, a small proportion develop severe disease requiring ICU admission and prolonged ventilation, although fatal outcome is overall rare. The data also reflect the current uncertainties regarding specific treatment options, highlighting that additional data on antiviral and immunomodulatory drugs are urgently needed. Funding ptbnet is supported by Deutsche Gesellschaft für Internationale Zusammenarbeit

On a Transvariation Based Measure of Group Separability

In this paper, the potentialities of transvariation (Gini, 1959) in measuring the separation between two groups of multivariate observations are explored. With this aim, a modified version of Gini’s notion of multidimensional transvariation is proposed. It allows to derive a measure of group separation, which can be profitably used in discriminating between two groups. The performance of the measure is tested through simulation experiments. The results show that the proposed measure is not sensitive to distributional assumptions and highlight its robustness against outliers

A dimensionally reduced finite mixture model for multilevel data

Recently, different mixture models have been proposed for multilevel data, generally requiring the local independence assumption. In this work, this assumption is relaxed by allowing each mixture component at the lower level of the hierarchical structure to be modeled according to a multivariate Gaussian distribution with a non-diagonal covariance matrix. For high-dimensional problems, this solution can lead to highly parameterized models. In this proposal, the trade-off between model parsimony and flexibility is governed by assuming a latent factor generative model.Cluster analysis Factor mixture model Dimension reduction EM-algorithm Multilevel latent class analysis

Risk factors and action thresholds for the novel coronavirus pandemic. Insights from the Italian Society of Nephrology COVID-19 Survey

Over 80% (365/454) of the nation's centers participated in the Italian Society of Nephrology COVID-19 Survey. Out of 60,441 surveyed patients, 1368 were infected as of April 23rd, 2020. However, center-specific proportions showed substantial heterogeneity. We therefore undertook new analyses to identify explanatory factors, contextual effects, and decision rules for infection containment