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arXiv 2022年

作者： Köber, Göran Kalisch, Raffael Puhlmann, Lara Chmitorz, Andrea Schick, Anita Binder, Harald Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Germany Freiburg Center for Data Analysis and Modelling University of Freiburg Germany Leibniz Institute for Resilience Research Mainz Germany Neuroimaging Center Focus Program Translational Neuroscience Johannes Gutenberg University Medical Center Mainz Germany Faculty of Social Work Education and Nursing Sciences University Esslingen Germany Department of Public Mental Health Central Institute of Mental Health Medical Faculty Mannheim Heidelberg University Germany

When modeling longitudinal biomedical data, often dimensionality reduction as well as dynamic modeling in the resulting latent representation is needed. This can be achieved by artificial neural networks for dimension reduction, and differential equations for dynamic modeling of individual-level trajectories. However, such approaches so far assume that parameters of individual-level dynamics are constant throughout the observation period. Motivated by an application from psychological resilience research, we propose an extension where different sets of differential equation parameters are allowed for observation sub-periods. Still, estimation for intra-individual sub-periods is coupled for being able to fit the model also with a relatively small dataset. We subsequently derive prediction targets from individual dynamic models of resilience in the application. These serve as interpretable resilience-related outcomes, to be predicted from characteristics of individuals, measured at baseline and a follow-up time point, and selecting a small set of important predictors. Our approach is seen to successfully identify individual-level parameters of dynamic models that allows us to stably select predictors, i.e., resilience factors. Furthermore, we can identify those characteristics of individuals that are the most promising for updates at follow-up, which might inform future study design. This underlines the usefulness of our proposed deep dynamic modeling approach with changes in parameters between observation sub-periods. © 2022, CC BY-NC-SA.

关键词： Differential equations

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Non-parametric model-based estimation of the effective reproduction number for SARS-CoV-2

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AIP Conference Proceedings 2023年第1期2872卷

作者： Jacques Hermes Marcus Rosenblatt Christian Tönsing Jens Timmer Institute of Physics University of Freiburg Hermann-Herder-Str. 3 79104 Freiburg Germany Freiburg Center for Data Analysis and Modelling (FDM) University of Freiburg Ernst-Zermelo-Str. 1 79104 Freiburg Germany Centre for Integrative Biological Signalling Studies (CIBSS) University of Freiburg Schänzlestr. 18 79104 Freiburg Germany

Viral outbreaks, such as the COVID-19 pandemic, are commonly described by compartmental models by means of ordinary differential equation (ODE) systems. The parameter values of these ODE models are typically unknown and need to be estimated based on accessible data. In order to describe realistic pandemic scenarios with strongly varying situations, these model parameters need to be assumed as time-dependent. While parameter estimation for the typical case of time-constant parameters does not pose larger issues, the determination of time-dependent parameters, e.g. the transition rates of compartmental models, remains notoriously difficult, since the function class of these time-dependent parameters is ***, we propose a combination of an Augmented Kalman Smoother with an Expectation-Maximization algorithm to simultaneously estimate all time-dependent parameters in an SIRD compartmental model. The approach is applicable to general systems of ODEs with time-varying parameters. In particular, it does not require prior knowledge on model parameters or any further assumptions on the function class of the time-dependencies of the ODE. In contrast to other approaches, no assumptions on the parameterization of the serial interval distribution are required for the estimation of SIRD-model parameters. With the presented method, we are able to adequately describe time series of COVID-19 data in Germany including strong fluctuations and multiple waves, and to give non-parametric model-based time course estimates for the effective reproduction number.

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Causal inference with multi-state models-estimands and estimators of the population-attributable fraction

arXiv

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arXiv 2019年

作者： Von Cube, Maja Schumacher, Martin Wolkewitz, Martin Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Freiburg Germany Freiburg Center for Data Analysis and Modelling University of Freiburg Freiburg Germany

The population-attributable fraction (PAF) is a popular epidemiological measure for the burden of a harmful exposure within a population. It is often interpreted causally as proportion of preventable cases after an elimination of exposure. Originally, the PAF has been defined for cohort studies of fixed length with a baseline exposure or cross-sectional studies. An extension of the definition to complex time-to-event data is not straightforward. We revise the proposed approaches in literature and provide a clear concept of the PAF for these data situations. The conceptualization is achieved by a proper differentiation between estimands and estimators as well as causal effect measures and measures of association. keywords:Causal effect measure, Competing risks, Excess fraction, Measure of association, Nosocomial infection, Time-dependent exposure. Copyright © 2019, The Authors. All rights reserved.

关键词： Risk assessment

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Development of Powerly, unguided mobile app intervention preventing postpartum depression and anxiety & study protocol of randomized clinical trial

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Internet Interventions 2025年 41卷

作者： Marta A. Marciniak Judith Rohde Kenneth S.L. Yuen Harald Binder Henrik Walter Matthias J. Wieser Raffael Kalisch Karin Roelofs Birgit Kleim Department of Psychology Education and Child Studies Erasmus University Rotterdam Rotterdam the Netherlands Healthy Longevity Center University of Zurich Zurich Switzerland Department of Psychology University of Zurich Zurich Switzerland Department of Psychiatry Psychotherapy and Psychosomatics Psychiatric University Hospital Zurich Switzerland Neuroimaging Center (NIC) Focus Program Translational Neuroscience (FTN) Johannes Gutenberg University Medical Center Mainz Germany Leibniz Institute for Resilience Research (LIR) Mainz Germany Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Freiburg Germany Freiburg Center for Data Analysis and Modelling University of Freiburg Freiburg Germany Charité - Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin Humboldt-Universität zu Berlin Berlin Institute of Health Berlin Germany Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen the Netherlands Behavioral Science Institute Radboud University Nijmegen the Netherlands

Background Up to 30 % of pregnant individuals experience high levels of stress. At the same time, 15–20 % of new mothers develop postpartum depression, and 25–35 % experience postpartum anxiety. Mobile applications have the potential to provide an accessible, scalable solution to these mental health challenges. However, previous evidence indicates that none of the commercially available apps for perinatal depression and anxiety have been rigorously evaluated through randomized clinical trials (RCTs), and the quality of these apps remains moderate. In response to this gap, we aim to develop and empirically evaluate Powerly, a mobile app intervention designed to prevent postpartum depression and anxiety. Methods We will conduct a two-arm RCT with 140 healthy pregnant participants to assess the impact of Powerly use compared to care as usual (CAU). Powerly is based on cognitive behavioral therapy techniques and developed in consultation with stakeholders, including healthcare professionals and pregnant individuals. It will offer personalized psychological support tailored to users' needs for four weeks. Self-reported mental health assessments will be collected at baseline, after four weeks of app use, and six weeks postpartum. Discussion We anticipate that participants using Powerly will demonstrate significant improvements in mental health outcomes, including reduced rates of postpartum depression, compared to the CAU group. Additionally, we expect positive changes in emotion regulation, resilience, and mother and child outcomes, such as enhanced maternal bonding and a more positive birth experience. If proved effective, Powerly can offer a scalable, publicly accessible solution for pregnant individuals in need. Trial registration NCT06610552

关键词： Pregnancy Postpartum depression Postpartum anxiety Stress Mobile health Ecological momentary intervention

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The population-attributable fraction for time-dependent exposures using dynamic prediction and landmarking

arXiv

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arXiv 2019年

作者： von Cube, Maja Schumacher, Martin Putter, Hein Timsit, Jéan-François de Velde, Cornelis van Wolkewitz, Martin Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Freiburg Germany Freiburg Center for Data Analysis and Modelling University of Freiburg Freiburg Germany Leiden University Medical Centre Department of Medical Statistics and Bioinformatics UMR 1137 IAME Inserm Université Paris Diderot Paris France APHP Medical and Infectious Diseases ICU Bichat Hospital Paris France Leiden University Medical Centre Department of Surgery Leiden Netherlands

The public health impact of a harmful exposure can be quantified by the population-attributable fraction (PAF). The PAF describes the attributable risk due to an exposure and is often interpreted as the proportion of preventable cases if the exposure could be extinct. Difficulties in the definition and interpretation of the PAF arise when the exposure of interest depends on time. Then, the definition of exposed and unexposed individuals is not straightforward. We propose dynamic prediction and landmarking to define and estimate a PAF in this data situation. Two estimands are discussed which are based on two hypothetical interventions that could prevent the exposure in different ways. Considering the first estimand, at each landmark the estimation problem is reduced to a time-independent setting. Then, estimation is simply performed by using a generalized-linear model accounting for the current exposure state and further (time-varying) covariates. The second estimand is based on counterfactual outcomes, estimation can be performed using pseudo-values or inverse-probability weights. The approach is explored in a simulation study and applied on two data examples. First, we study a large French database of intensive care unit patients to estimate the population-benefit of a pathogen-specific intervention that could prevent ventilator-associated pneumonia caused by the pathogen Pseudomonas aeruginosa. Moreover, we quantify the population-attributable burden of locoregional and distant recurrence in breast cancer patients. Copyright © 2019, The Authors. All rights reserved.

关键词： Intensive care units

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Distributed Multivariate Regression Modeling For Selecting Biomarkers Under data Protection Constraints

arXiv

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arXiv 2018年

作者： Zöller, Daniela Binder, Harald Institute of Medical Biometry and Statistics Faculty of Medicine Medical Center University of Freiburg Stefan-Meier-Str. 26 Freiburg79104 Germany Freiburg Center for Data Analysis and Modelling University of Freiburg Germany

The discovery of clinical biomarkers requires large patient cohorts and is aided by a pooled data approach across institutions. In many countries, data protection constraints, especially in the clinical environment, forbid the exchange of individual-level data between different research institutes, impeding the conduct of a joint analyses. To circumvent this problem, only non-disclosive aggregated data is exchanged, which is often done manually and requires explicit permission before transfer, i.e., the number of data calls and the amount of data should be limited. This does not allow for more complex tasks such as variable selection, as only simple aggregated summary statistics are typically transferred. Other methods have been proposed that require more complex aggregated data or use input data perturbation, but these methods can either not deal with a high number of biomarkers or lose information. Here, we propose a multivariable regression approach for identifying biomarkers by automatic variable selection based on aggregated data in iterative calls, which can be implemented under data protection constraints. The approach can be used to jointly analyze data distributed across several locations. To minimize the amount of transferred data and the number of calls, we also provide a heuristic variant of the approach. When performing global data standardization, the proposed method yields the same results as pooled individual-level data analysis. In a simulation study, the information loss introduced by local standardization is seen to be minimal. In a typical scenario, the heuristic decreases the number of data calls from more than 10 to 3, rendering manual data releases feasible. To make our approach widely available for application, we provide an implementation of the heuristic version incorporated in the dataSHIELD framework. Copyright © 2018, The Authors. All rights reserved.

关键词： Biomarkers

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The population-attributable fraction for time-dependent exposures and competing risks – A discussion on estimands

arXiv

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arXiv 2019年

作者： von Cube, Maja Schumacher, Martin Bailly, Sébastien Timsit, Jean-François Lepape, Alain Savey, Anne Machut, Anais Wolkewitz, Martin Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Freiburg Germany Freiburg Center for Data Analysis and Modelling University of Freiburg Freiburg Germany HP2 laboratory University of Grenoble Alpes Grenoble France Department of Physiology and Sleep Grenoble Alpes University Hospital Grenoble France UMR 1137 IAME Inserm Universite Paris Diderot Paris France APHP Medical and Infectious Diseases ICU Bichat Hospital Paris France CPIAS Auvergne-Rhone-Alpes Hospices Civils de Lyon Lyon France Clinical research Unit Critical care Lyon Sud University Hospital Hospices Civils de Lyon Lyon France Inserm U1111 CNRS UMR5308 ENS de Lyon UCBL1 Lyon France Ernst-Zermelo Strasse 1 Freiburg 79104 Germany

The population-attributable fraction (PAF) quantifies the public health impact of a harmful exposure. Despite being a measure of significant importance an estimand accommodating complicated time-to-event data is not clearly defined. We discuss current estimands of the PAF used to quantify the public health impact of an internal time-dependent exposure for data subject to competing outcomes. To overcome some limitations, we proposed a novel estimand which is based on dynamic prediction by landmarking. In a profound simulation study, we discuss interpretation and performance of the various estimands and their estimators. The methods are applied to a large french database to estimate the health impact of ventilator-associated pneumonia for patients in intensive care. Copyright © 2019, The Authors. All rights reserved.

关键词： Public health

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Feature selection in high dimensional EEG features spaces for epileptic seizure prediction

Feature selection in high dimensional EEG features spaces fo...

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作者： Direito, Bruno Duarte, João Teixeira, César Schelter, Björn Le Van Quyen, Michel Schulze-Bonhage, Andreas Sales, Francisco Dourado, António Center for Informatics and Systems of the University of Coimbra University of Coimbra Portugal IBILI - Institute of Biomedical Research in Light and Image Faculty of Medicine University of Coimbra Portugal Centre de Recherche Institut du Cerveau et de la Moelle Epinière-INSERM Hôpital de la Pitié-Salpêtrière Germany Freiburg Center for Data Analysis and Modelling Albert-Ludwigs-Universität Freiburg Germany Epilepsy Center University Hospital of Freiburg Germany Hospitals of the University of Coimbra Portugal

ISBN: (纸本)9783902661937

Digital signal processing of Electroencephalogram (EEG) can support the diagnosis and alarming for the benefit of humans. About one third of all epileptic patients suffer from refractory epilepsy;seizure prediction based on the EEG information content is an area of intense activity since at least twenty years. In this paper we analyze the high dimensional feature space created by a variety of feature extraction methods for prediction of epileptic seizures. We combined features selection algorithm minimum redundancy maximum relevance (mRMR) and Support Vector Machines (SVMs) architectures to study the best features set for seizure prediction. We present the comparison between the classification results obtained by a feature set composed by 147 features and a reduced set based on the first 20-ranked features using mRMR scores. We critically discuss the composition of the feature subset. The results suggest some patient specificity in features and channel selection. The best models lead us to hypothesize the preference for wider preictal periods. © 2011 IFAC.

关键词： Classification (of information)

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Feature selection in high dimensional EEG features spaces for epileptic seizure prediction

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IFAC Proceedings Volumes 2011年第1期44卷 6206-6211页

作者： Bruno Direito João Duarte César Teixeira Björn Schelter Michel Le Van Quyen Andreas Schulze-Bonhage Francisco Sales António Dourado Center for Informatics and Systems of the University of Coimbra University of Coimbra Portugal IBILI – Institute of Biomedical Research in Light and Image Faculty of Medicine of the University of Coimbra Portugal Centre de Recherche de l'Institut du Cerveau et de la Moelle Epinière-INSERM Unité Mixte de Recherche en Santé 975-Centre National de la Recherche Scientifique Unité Mixte de Recherche 7225 Hôpital de la Pitié-Salpětrière Freiburg Center for Data Analysis and Modelling Albert-Ludwigs-Universität Freiburg Germany Epilepsy Center University Hospital of Freiburg Germany Hospitals of the University of Coimbra Portugal

Abstract Digital signal processing of Electroencephalogram (EEG) can support the diagnosis and alarming for the benefit of humans. About one third of all epileptic patients suffer from refractory epilepsy; seizure prediction based on the EEG information content is an area of intense activity since at least twenty years. In this paper we analyze the high dimensional feature space created by a variety of feature extraction methods for prediction of epileptic seizures. We combined features selection algorithm minimum redundancy maximum relevance (mRMR) and Support Vector Machines (SVMs) architectures to study the best features set for seizure prediction. We present the comparison between the classification results obtained by a feature set composed by 147 features and a reduced set based on the first 20-ranked features using mRMR scores. We critically discuss the composition of the feature subset. The results suggest some patient specificity in features and channel selection. The best models lead us to hypothesize the preference for wider preictal periods.

关键词： Classification Methods Classification Feature Selection Seizure Prediction Epilepsy

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