Modélisation des données d'enquêtes cas-cohorte par imputation multiple (Application en épidémiologie cardio-vasculaire.)

Les estimateurs pondérés généralement utilisés pour analyser les enquêtes cas-cohorte ne sont pas pleinement efficaces. Or, les enquêtes cas-cohorte sont un cas particulier de données incomplètes où le processus d'observation est contrôlé par les organisateurs de l'étude. Ainsi, des méthodes d'analyse pour données manquant au hasard (MA) peuvent être pertinentes, en particulier, l'imputation multiple, qui utilise toute l'information disponible et permet d'approcher l'estimateur du maximum de vraisemblance partielle.Cette méthode est fondée sur la génération de plusieurs jeux plausibles de données complétées prenant en compte les différents niveaux d'incertitude sur les données manquantes. Elle permet d'adapter facilement n'importe quel outil statistique disponible pour les données de cohorte, par exemple, l'estimation de la capacité prédictive d'un modèle ou d'une variable additionnelle qui pose des problèmes spécifiques dans les enquêtes cas-cohorte. Nous avons montré que le modèle d'imputation doit être estimé à partir de tous les sujets complètement observés (cas et non-cas) en incluant l'indicatrice de statut parmi les variables explicatives. Nous avons validé cette approche à l'aide de plusieurs séries de simulations: 1) données complètement simulées, où nous connaissions les vraies valeurs des paramètres, 2) enquêtes cas-cohorte simulées à partir de la cohorte PRIME, où nous ne disposions pas d'une variable de phase-1 (observée sur tous les sujets) fortement prédictive de la variable de phase-2 (incomplètement observée), 3) enquêtes cas-cohorte simulées à partir de la cohorte NWTS, où une variable de phase-1 fortement prédictive de la variable de phase-2 était disponible. Ces simulations ont montré que l'imputation multiple fournissait généralement des estimateurs sans biais des risques relatifs. Pour les variables de phase-1, ils approchaient la précision obtenue par l'analyse de la cohorte complète, ils étaient légèrement plus précis que l'estimateur calibré de Breslow et coll. et surtout que les estimateurs pondérés classiques. Pour les variables de phase-2, l'estimateur de l'imputation multiple était généralement sans biais et d'une précision supérieure à celle des estimateurs pondérés classiques et analogue à celle de l'estimateur calibré. Les résultats des simulations réalisées à partir des données de la cohorte NWTS étaient cependant moins bons pour les effets impliquant la variable de phase-2 : les estimateurs de l'imputation multiple étaient légèrement biaisés et moins précis que les estimateurs pondérés. Cela s'explique par la présence de termes d'interaction impliquant la variable de phase-2 dans le modèle d'analyse, d'où la nécessité d'estimer des modèles d'imputation spécifiques à différentes strates de la cohorte incluant parfois trop peu de cas pour que les conditions asymptotiques soient réunies.Nous recommandons d'utiliser l'imputation multiple pour obtenir des estimations plus précises des risques relatifs, tout en s'assurant qu'elles sont analogues à celles fournies par les analyses pondérées. Nos simulations ont également montré que l'imputation multiple fournissait des estimations de la valeur prédictive d'un modèle (C de Harrell) ou d'une variable additionnelle (différence des indices C, NRI ou IDI) analogues à celles fournies par la cohorte complèteThe weighted estimators generally used for analyzing case-cohort studies are not fully efficient. However, case-cohort surveys are a special type of incomplete data in which the observation process is controlled by the study organizers. So, methods for analyzing Missing At Random (MAR) data could be appropriate, in particular, multiple imputation, which uses all the available information and allows to approximate the partial maximum likelihood estimator.This approach is based on the generation of several plausible complete data sets, taking into account all the uncertainty about the missing values. It allows adapting any statistical tool available for cohort data, for instance, estimators of the predictive ability of a model or of an additional variable, which meet specific problems with case-cohort data. We have shown that the imputation model must be estimated on all the completely observed subjects (cases and non-cases) including the case indicator among the explanatory variables. We validated this approach with several sets of simulations: 1) completely simulated data where the true parameter values were known, 2) case-cohort data simulated from the PRIME cohort, without any phase-1 variable (completely observed) strongly predictive of the phase-2 variable (incompletely observed), 3) case-cohort data simulated from de NWTS cohort, where a phase-1 variable strongly predictive of the phase-2 variable was available. These simulations showed that multiple imputation generally provided unbiased estimates of the risk ratios. For the phase-1 variables, they were almost as precise as the estimates provided by the full cohort, slightly more precise than Breslow et al. calibrated estimator and still more precise than classical weighted estimators. For the phase-2 variables, the multiple imputation estimator was generally unbiased, with a precision better than classical weighted estimators and similar to Breslow et al. calibrated estimator. The simulations performed with the NWTS cohort data provided less satisfactory results for the effects where the phase-2 variable was involved: the multiple imputation estimators were slightly biased and less precise than the weighted estimators. This can be explained by the interactions terms involving the phase-2 variable in the analysis model and the necessity of estimating specific imputation models in different strata not including sometimes enough cases to satisfy the asymptotic conditions. We advocate the use of multiple imputation for improving the precision of the risk ratios estimates while making sure they are similar to the weighted estimates.Our simulations also showed that multiple imputation provided estimates of a model predictive value (Harrell's C) or of an additional variable (difference of C indices, NRI or IDI) similar to those obtained from the full cohort.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

Platelet-derived exosomes induce endothelial cell apoptosis through peroxynitrite generation: experimental evidence for a novel mechanism of septic vascular dysfunction

Abstract\ud \ud \ud \ud Introduction\ud \ud Several studies link hematological dysfunction to severity of sepsis. Previously we showed that platelet-derived microparticles from septic patients induce vascular cell apoptosis through the NADPH oxidase-dependent release of superoxide. We sought to further characterize the microparticle-dependent vascular injury pathway.\ud \ud \ud \ud Methods\ud \ud During septic shock there is increased generation of thrombin, TNF-α and nitric oxide (NO). Human platelets were exposed for 1 hour to the NO donor diethylamine-NONOate (0.5 μM), lipopolysaccharide (LPS; 100 ng/ml), TNF-α (40 ng/ml), or thrombin (5 IU/ml). Microparticles were recovered through filtration and ultracentrifugation and analyzed by electron microscopy, flow cytometry or Western blotting for protein identification. Redox activity was characterized by lucigenin (5 μM) or coelenterazine (5 μM) luminescence and by 4,5-diaminofluorescein (10 mM) and 2',7'-dichlorofluorescein (10 mM) fluorescence. Endothelial cell apoptosis was detected by phosphatidylserine exposure and by measurement of caspase-3 activity with an enzyme-linked immunoassay.\ud \ud \ud \ud Results\ud \ud Size, morphology, high exposure of the tetraspanins CD9, CD63, and CD81, together with low phosphatidylserine, showed that platelets exposed to NONOate and LPS, but not to TNF-α or thrombin, generate microparticles similar to those recovered from septic patients, and characterize them as exosomes. Luminescence and fluorescence studies, and the use of specific inhibitors, revealed concomitant superoxide and NO generation. Western blots showed the presence of NO synthase II (but not isoforms I or III) and of the NADPH oxidase subunits p22phox, protein disulfide isomerase and Nox. Endothelial cells exposed to the exosomes underwent apoptosis and caspase-3 activation, which were inhibited by NO synthase inhibitors or by a superoxide dismutase mimetic and totally blocked by urate (1 mM), suggesting a role for the peroxynitrite radical. None of these redox properties and proapoptotic effects was evident in microparticles recovered from platelets exposed to thrombin or TNF-α.\ud \ud \ud \ud Conclusion\ud \ud We showed that, in sepsis, NO and bacterial elements are responsible for type-specific platelet-derived exosome generation. Those exosomes have an active role in vascular signaling as redox-active particles that can induce endothelial cell caspase-3 activation and apoptosis by generating superoxide, NO and peroxynitrite. Thus, exosomes must be considered for further developments in understanding and treating vascular dysfunction in sepsis.LRL and MJ have research grants from Fundação de Amparo a Pesquisa do Estado de São Paulo – FAPESP. MJ received a research grant from Sociedade Beneficente Israelita-Brasileira Hospital Albert Einstein.LRL and MJ have research grants from Fundação de Amparo a Pesquisa do Estado de São Paulo – FAPESP. MJ received a research grant from Sociedade Beneficente IsraelitaBrasileira Hospital Albert Einstein

Characterization of the Outer Coast Tuff Formation - A way to unravelling the magmatic processes preceding and triggering Deception Island's caldera-forming eruption (Antarctica)

Deception Island (South Shetland Islands), discovered in 1820, is one of the most active volcanoes in Antarctica with more than 20 eruptions (including the historic eruptions of 1967, 1969 and 1970) and three documented volcanic unrest events (1992, 1999 and 2014-15) over the past two centuries. Deception Island currently hosts two scientific bases, which operate every year during the Austral summer, and is also one of the most popular tourist destinations in Antarctica. The island is a composite volcano with a centrally located caldera of 8.5 x 10 km dated at 3,980 ± 125 yr. BP. During the caldera-forming event, between 30 and 60 km3 (Dense Rock Equivalent-DRE) of magma, erupted in the form of dense basaltic-andesitic pyroclastic density current deposits. During the last decades, Deception Island has been intensively investigated but some aspects regarding the magmatic processes associated with the formation of its caldera collapse are still under research and debate. For instance, characterizing the magmatic conditions and processes that triggered the huge explosive event is crucial to understand the past (and in turn the future) magmatic and volcanic evolution of the island

Multiple imputation for estimating hazard ratios and predictive abilities in case-cohort surveys

<p>Abstract</p> <p>Background</p> <p>The weighted estimators generally used for analyzing case-cohort studies are not fully efficient and naive estimates of the predictive ability of a model from case-cohort data depend on the subcohort size. However, case-cohort studies represent a special type of incomplete data, and methods for analyzing incomplete data should be appropriate, in particular multiple imputation (MI).</p> <p>Methods</p> <p>We performed simulations to validate the MI approach for estimating hazard ratios and the predictive ability of a model or of an additional variable in case-cohort surveys. As an illustration, we analyzed a case-cohort survey from the Three-City study to estimate the predictive ability of D-dimer plasma concentration on coronary heart disease (CHD) and on vascular dementia (VaD) risks.</p> <p>Results</p> <p>When the imputation model of the phase-2 variable was correctly specified, MI estimates of hazard ratios and predictive abilities were similar to those obtained with full data. When the imputation model was misspecified, MI could provide biased estimates of hazard ratios and predictive abilities. In the Three-City case-cohort study, elevated D-dimer levels increased the risk of VaD (hazard ratio for two consecutive tertiles = 1.69, 95%CI: 1.63-1.74). However, D-dimer levels did not improve the predictive ability of the model.</p> <p>Conclusions</p> <p>MI is a simple approach for analyzing case-cohort data and provides an easy evaluation of the predictive ability of a model or of an additional variable.</p

Descifrando los procesos magmáticos desencadenantes de la formación de la caldera en Isla Decepción (Antártida)

Isla Decepción (Islas Shetland del Sur) es uno de los volcanes más activos de la Antártida, con más de 20 erupciones en los últimos 200 años (las más recientes en 1967, 1969 y 1970) y tres episodios de unrest documentados (1992, 1999 y 2014-15). La isla está formada por un sistema volcánico compuesto con una caldera central de 8,5 x 10 km datada en unos 3.980 ± 125 años. Durante la formación de la caldera de colapso, se emitieron entre 30 y 60 km3 (Dense Rock Equivalent-DRE) de magma basáltico-andesítico en forma de flujos piroclásticos, que dieron lugar a la Outer Coast Tuff Formation (OCTF), la principal unidad sin-caldera. Caracterizar las condiciones magmáticas y los procesos que desencadenaron el evento explosivo es crucial para entender el pasado (y futuro) de la evolución magmática de la isla. El objetivo de este trabajo es establecer, a partir de la petrología y la geoquímica, las condiciones y los procesos magmáticos que tuvieron lugar antes y durante la formación de la caldera de colapso. Los resultados preliminares confirman la existencia de dos magmas coexistiendo e interactuando antes (y durante) la erupción caldérica y procesos de cristalización fraccionada. Esta investigación es parte de las iniciativas de investigación POLARCSIC y PTIVolcan

Neighbors' use of water and sanitation facilities can affect children's health:a cohort study in Mozambique using a spatial approach

Background Impact evaluation of most water, sanitation and hygiene (WASH) interventions in health are user-centered. However, recent research discussed WASH herd protection - community WASH coverage could protect neighboring households. We evaluated the effect of water and sanitation used in the household and by household neighbors in children's morbidity and mortality using recorded health data. Methods We conducted a retrospective cohort including 61,333 children from a district in Mozambique during 2012-2015. We obtained water and sanitation household data and morbidity data from Manhiça Health Research Centre surveillance system. To evaluate herd protection, we estimated the density of household neighbors with improved facilities using a Kernel Density Estimator. We fitted negative binomial adjusted regression models to assess the minimum children-based incidence rates for every morbidity indicator, and Cox regression models for mortality. Results Household use of unimproved water and sanitation displayed a higher rate of outpatient visit, diarrhea, malaria, and anemia. Households with unimproved water and sanitation surrounded by neighbors with improved water and sanitation high coverage were associated with a lower rate of outpatient visit, malaria, anemia, and malnutrition. Conclusion Household and neighbors' access to improve water and sanitation can affect children's health. Accounting for household WASH and herd protection in interventions' evaluation could foster stakeholders' investment and improve WASH related diseases control

Publishing data to support the fight against human vector-borne diseases

Vector-borne diseases are responsible for more than 17% of human cases of infectious diseases. In most situations, effective control of debilitating and deadly vector-bone diseases (VBDs), such as malaria, dengue, chikungunya, yellow fever, Zika and Chagas requires up-to-date, robust and comprehensive information on the presence, diversity, ecology, bionomics and geographic spread of the organisms that carry and transmit the infectious agents. Huge gaps exist in the information related to these vectors, creating an essential need for campaigns to mobilise and share data. The publication of data papers is an effective tool for overcoming this challenge. These peer-reviewed articles provide scholarly credit for researchers whose vital work of assembling and publishing well-described, properly-formatted datasets often fails to receive appropriate recognition. To address this, GigaScience 's sister journal GigaByte partnered with the Global Biodiversity Information Facility (GBIF) to publish a series of data papers, with support from the Special Programme for Research and Training in Tropical Diseases (TDR), hosted by the World Health Organisation (WHO). Here we outline the initial results of this targeted approach to sharing data and describe its importance for controlling VBDs and improving public health