Foto: Rohmann

Intensive Short Courses an der BSPH

Die Teilnehmerinnen und Teilnehmer der Intensive Short Courses erwerben kompaktes Wissen in Public Health und Epidemiologie. Die Vermittlung von Methodenkenntnissen ist ein wichtiger Schwerpunkt.

Die Kurse richten sich an Doktoranden und Studierende aus Public Health Masterstudiengängen im In- und Ausland.

Sie werden von Dozierenden renommierter Hochschulen und Forschungseinrichtungen geleitet. 

Sie befinden sich hier:

Intensive Short Courses:

  • werden in der Regel in englischer Sprache angeboten
  • haben grundsätzlich Lehrcharakter
  • sind mit ECTS ausgewiesen
  • erfordern eine Prüfungsleistung

Intensive Short Courses 2019

Medical Informatics, 05.08. - 09.08.2019

Date: 5.8.-9.8.2019

Lecturer:

Felix Balzer, Professor at Charité - Universitätsmedizin Berlin and others

    Topics:

    • Introduction to Health Information Technology
    • Information Extraction from Electronic Health Records
    • Terminologies and Ontologies
    • Introduction to programming
    • Medical imaging, pattern recognition
    • Analysis of large data-sets
    • Data protection and regulatory aspects

    Prerequisites:

    • Basic knowledge of epidemiology and biostatistics

    Fees:

    • 750 €
    • 510 € for enrolled students (proof required)

    Examination: Several multiple Choice quizzes

    3 ECTS

    Registration:

    Please send an e-mail to Tanja Te Gude

    Tel. +49 30 450 570 812

    Tel. +49 30 450 570 812

    Email

    Advanced Epidemiologic Methods: Rethinking Basic Epidemiologic Concepts, 12.8. - 16.8.2019

    Photo: privat

    Date: 12.8.-16.8.2019

    Lecturer:

    Matthew Fox, Professor in the Department of Global Health and Epidemiology at Boston University

      Learning objectives:

      By the end of this week, participants should be able to:

      • Use the sufficient cause model, counterfactual susceptibility type model, and a causal graph to assist with the design or analysis of an epidemiologic study.
      • Calculate adjusted measures of effect and select those that, when collapsible, correspond to the no-confounding condition. Use the adjusted measures of effect to estimate the direction and magnitude of confounding.
      • Distinguish effect measure modification, interdependence, and statistical interaction from one another as separate - but related - concepts of interaction.
      • Identify the likely magnitude and direction of bias due to misclassification of exposure, outcomes, confounders and modifiers. Weigh the advantages and disadvantages of significance testing.
      • Compare the advantages and disadvantages of frequentist and Bayesian approaches to analysis of a single study, to eveidence, and to changing your mind.

      Prerequisites:

      • Basic knowledge of epidemiology and biostatistics

      Fees:

      • 750 €
      • 510 € for enrolled students (proof required)

      3 ECTS

      Registration:

      Please send an e-mail to Tanja Te Gude

      Tel. +49 30 450 570 812

      Tel. +49 30 450 570 812

      Email

      Flyer:

      To download the flyer please click here

      Applied Digital Health, 19.08. - 23.08.2019

      Date: 19.8.-23.8.2019

      Lecturer:

      Felix Balzer, Professor at Charité - Universitätsmedizin Berlin and others

        Topics:

        • Interoperability and standards
        • Connected Health
        • Digital Surgery - Extended Reality (XR) and Robotics in Visceral Surgery
        • Human factors (tbd)
        • Telemedicine/Remote Patient Monitoring
        • Data management and registries

         

        Prerequisites:

        • Basic knowledge of epidemiology and biostatistics

        Fees:

        • 750 €
        • 510 € for enrolled students (proof required)

        Examination: Several multiple Choice quizzes

        3 ECTS

        Registration:

        Please send an e-mail to Tanja Te Gude

        Tel. +49 30 450 570 812

        Tel. +49 30 450 570 812

        Email

        Advanced Epidemiologic Methods: Mastering R for Epidemiologic Research, 02.09. - 06.09.2019

        The course is for researchers, public health professionals, epidemiologists, and clinicians who want to improve their R coding skills, who want to learn modern tools in the R ecosystem, like the tidyverse and Shiny, or who want to get started writing software in R.

        Date: 02.09. - 06.09.2019

        Lecturer: Malcolm Barrett, MPH

        Malcolm Barrett is a PhD candidate in epidemiology at the University of Southern California. He studies vision loss and eye diseases that affect vision, like diabetic retinopathy. Specifically, he works on methods to improve the study of how vision impacts quality of life, including tools from psychometrics and causal inference, to make vision-specific quality of life analyses more accurate and more interpretable. In addition to applied research, Malcolm also develops R packages for epidemiologic and biostatistical methods, teaches R, and organizes the Los Angeles R Users Group. He regularly contributes to open source software, including favorite community projects like ggplot2 and R Markdown.

        Learning objectives:

        At the end of the week, participants will have:

        • Mastered the tidyverse, a set of principled tools for data science. The tidyverse is a friendly, readable, and fast set of packages intended to work well together, to improve code readability, and to make analyses more reproducible.
        • Written dynamic documents using best practices for reproducible research, including using R Markdown. R Markdown intertwines code and text so that reports and articles are fully reproducible and exportable to PDF, HTML, Word, and more. R Markdown also has excellent support for citation management and formatting for journals.
        • Modeled simple statistical and causal problems using both classical regression (linear, logistic) and G-methods.
        • Written robust functions, programmed with functions, and created a basic R package.
        • Built ready-to-share web applications entirely in R using the Shiny framework.

        Pre-requisites:

        Basic epidemiology and statistics. Some experience with R programming will be helpful, but those new to R can take a free introductory course on DataCamp: https://www.datacamp.com/courses/free-introduction-to-r  

        Materials:

        R for Data Science (https://r4ds.had.co.nz/) and Advanced R, ed. 2 (https://adv-r.hadley.nz/), both free online.

        Course Information:

        • Course language: English
        • ECTS points: 3
        • Course Fee: 510€ for students, 750€ for other participants

        Registration:

        Please send an e-mail to Tanja Te Gude

        Tel. +49 30 450 570 812

        Flyer:

        To download the flyer, please click here

        Intensive Short Courses 2018

        Advanced Epidemiologic Methods – causal research and prediction modeling, 20.8.-24.8.2018

        Date: 20.8.-24.8.2018

        Lecturer:

        • Rolf H.H. Groenwold, MD, PhD, Leiden University Medical Center (NL)
        • Maarten van Smeden, PhD, Leiden University Medical Center (NL)

        Learning objectives:

        By the end of this week, participants should be able to:

        • Critically assess the results of epidemiological studies on causal relationships or prediction models
        • Correctly define exposures and learn how to best represent them in models
        • Understand difference between various sources of bias (confounding, measurement error and missing data) and the way these biases may differentially affect studies on causal relationships and prediction models.
        • Describe key assumptions of methods used to control for (time-varying) confounding.
        • Describe key assumptions of methods used to handle missing observations.
        • Understand the reasons for and consequences of overfitting prediction models
        • Describe recent developments in the fields of causal research and prediction modelling

        Prerequisites:

        • Basic knowledge of epidemiology
        • Familiarity with R statistical software (for a short introduction see http://www.r-tutorial.nl/)

        Fees:

        • 750 €
        • 510 € for enrolled students (proof required)

        3 ECTS

         

        Registration Information:
        Tanja Te Gude
        Tel. +49 30 450 570 812
        Email

         

         

        Principles and Methods of Epidemiology: Critiquing the Medical Literature, 12.4. - 14.4.2018

        Julie Buring (photo: privat)
        Pamela Rist (photo:privat)

        Date: 12.4.-14.4.2018

        Thursday 12.4.: 2pm to 6pm; Friday 13.4.: 9am to 5pm; Saturday 14.4.: 9am to 12pm

        Lecturer:

        • Julie E. Buring, ScD, Professor of Medicine, Harvard Medical School and Professor of Epidemiology, Harvard T.H. Chan School of Public Health
        • Pamela Marie Rist, ScD, Assistant Professor of Medicine, Harvard Medical School

        Language: English

        Learning objectives:

        During this 3-day short course, participants will develop a solid foundation of applied principles and methods of epidemiology; use these to evaluate relevant clinical and public health questions; and refine their ability to critically analyze the epidemiologic, clinical, and public health literature. Special topics include exploring confounding and effect modification in context, the use of diagrams to understand confounding and bias, and a seminar on how to serve as a journal reviewer. The format will include lectures, group exercises, and seminars.

        Prerequisites:

        • Basic knowledge of epidemiology

        Fees:

        • 312,50 €
        • 212,50 € for enrolled students (proof required)

        ECTS: 1,25

        Program details: click here

        Registration Information:
        Tanja Te Gude
        Tel. +49 30 450 570 812

        About the lecturers:

        Julie E. Buring, ScD, Professor of Medicine, Harvard Medical School
        Prof. Julie E. Buring is Professor of Medicine at Harvard Medical School and Brigham and Women’s Hospital; and Professor of Epidemiology at the Harvard T.H. Chan School of Public Health. The primary focus of her research is on the prevention of chronic diseases, especially among women. Dr. Buring has been involved in the design, conduct, analysis, and interpretation of a number of large-scale randomized clinical trials of the primary prevention of cardiovascular disease, cancer and other chronic diseases. These include the Women’s Health Study, evaluating the preventive roles of aspirin and vitamin E; the Physicians' Health Study II, evaluating vitamin E, vitamin C, beta-carotene, and a multivitamin; and VITAL, an ongoing trial of vitamin D and fish oil. She serves as Chair of the Institutional Review Board of Harvard Medical School.( Edited from: www.edx.org/bio/julie-buring)

        Pamela Marie Rist, Sc.D. Assistant Professor, Harvard Medical School

        Dr. Pamela Rist, ScD, is an assistant professor of Medicine at the Harvard Medical School and an Instructor in Epidemiology at the Harvard T.H. Chan School of Public Health. Dr. Rist’s academic research interests are primarily focused on the epidemiology of cardiovascular disease (particularly stroke) and neurologic diseases (especially those with a vascular component) with the goal of identifying ways to reduce the morbidity burden associated with stroke, migraine, and cognitive decline. She has received teaching awards from the Harvard T.H. Chan School of Public Health and the Department of Epidemiology for her teaching. Edited from: www.hsph.harvard.edu/admissions/degree-programs/online-mph-in-epidemiology/faculty/

        Epidemiologie II, Prof. Dr. Andreas Stang, Februar - März 2018

        Im Februar und März 2018 findet der Epidemiologie II Kurs mit Prof. Andreas Stang statt. Das Modul ist Teil des Curriculums des Masters of Science in Epidemiologie der BSPH (Modul 4: Epidemiologie II), jedoch auch für externe Kursteilnehmer offen. Der Kurs vermittelt vertiefende Kenntnisse und Fertigkeiten in Konzepten und Methoden epidemiologischer Forschung. Unterrichtssprache ist Deutsch.

        Die Vorlesungsinhalte finden in SAS-Computer-Labs und Paper & Pencil Übungen praktische Anwendung. Das Modul ist eine Blockveranstaltung und besteht aus drei ganztägigen Blöcken (donnerstags bis samstags).
        Ziel des Moduls ist Vertiefung akademisch und praktisch erworbener epidemiologischer Kompetenzen von Wissenschaftlerinnen und Wissenschaftlern der Charité und anderen Forschungseinrichtungen.

        Termine: 1.2.2018, 15.02.2018 - 17.02.2018, 08.03.2018 - 10.03.2018, 15.03.2018 - 17.03.2018

        Dozierender: Prof. Dr. med. Andreas Stang, MPH
        Leiter des Zentrums für Klinische Epidemiologie (ZKE) am Institut für Medizinische Informatik, Biometrie & Epidemiologie (IMIBE), Universitätsklinikum Essen

        Inhalte:

        • Methodische Konzepte epidemiologischer Studien und epidemiologischer Assoziations- bzw. Effektmaße (Kohorten-, Fall-Kontroll-Studien) und korrespondierende Datenanalysen
        • Informations-, Selektionsbias und Confounding-Ansätze (klassische Definition, Kollapsibilitäts-Definition, kausale Diagramme)
        • Wahrscheinlichkeits(Dichte)-Verteilungen, Null Hypothesen Signifikanztesten, generalisierte lineare Modelle und stratifizierte Analysen
        • Praktische Anwendung von biometrischen Verfahren und vertieftes Theorieverständnis in SAS. (Alternative Softwareprogramme (z.B. SPSS, STATA, R etc.) können verwendet werden, finden aber keine Erläuterungen im Kurs.)

        Qualifikationsziele:

        • Teilnehmende können nach Besuch des Moduls:
        • Epidemiologische Methodenkenntnisse vertieft anwenden
        • Studiendesigns entsprechend epidemiologischer Forschungsfragen selbstständig entwickeln
        • Unterschiedliche Design-Optionen kritisch reflektieren und entsprechend des Forschungsziels begründet einsetzen
        • Daten epidemiologischer Studien in SAS auswerten und aufbereiten
        • Studienergebnisse wissenschaftlich präsentieren.

        Prüfungsleistung: Quiz nach jeder Kurseinheit, Open Book Take Home Exam

        Teilnahmevoraussetzungen: Grundkenntnisse der Epidemiologie, Biostatistik und SAS. Laptop mit SAS und ein wissenschaftlicher Taschenrechner.

        Teilnahmegebühren: Studierende: 850€, externe Teilnehmende: 1.250€

        ECTS: 5

        Lehrbuch:

        Rothman KJ, Greenland s, Lash T: Modern Epidemiology, 3rd edition
        Rothman KJ: Introduction to Epidemiology. 2nd edition, Oxford University Press, New York, 2012 (Grundlage für den Kurs).

        Kontakt: Tanja te Gude