Intensive Short Courses at the BSPH

Participants in the Intensive Short Courses acquire in-depth knowledge with a methodological focus on a specialized topic in public health and epidemiology.

  • Courses are aimed at students and external participants from Germany and abroad
  • Courses are held in cooperation with lecturers from renowned universities and research institutes from Germany and abroad
  • Courses are organised by the Institute of Public Health at Charité - Universitätsmedizin Berlin.

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Intensive Short Courses:

  • Usually offered in English
  • Award ECTS credits
  • Require a performance assessment

Unfortunately we are unable to offer funding assistance. Please contact scholarship opportunities such as DAAD directly to see if they have funding available.

Intensive Short Courses 2019

Medical Informatics, 05.8. - 09.8.2019

Lecturers:

  • Prof. Dr. Sylvia Thun, Berlin Institute of Health
  • Prof. Dr. Dr. Felix Balzer, Charité - Universitätsmedizin Berlin
  • Dipl.-Ing. Andreas Kofler, Charité - Universitätsmedizin Berlin
  • Prof. Dr. Tim Conrad, Free University Berlin & Zuse Institute Berlin
  • Dr. Martin Haase, Technical University Berlin

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 analytic background (statistics, epidemiology), basic computing skills

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

Flyer: To download the flyer, please click here.

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

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

    To open the Flyer please click here

    Applied Digital Health, 19.8. - 23.8.2019

    Date: 19.8.-23.8.2019

    Lecturers:

    • Dr. Dipl.-Vw. Josef Schepers, Berlin Institute of Health
    • Prof. Dr. Sylvia Thun, Berlin Institute of Health
    • Prof. Dr. Dr. Felix Balzer and team, Charité - Universitätsmedizin Berlin
    • Prof. Dr. Igor M. Sauer, Charité - Universitätsmedizin Berlin
    • Prof. Dr. Bert Arnrich, Hasso-Plattner-Institute
    • Dr. Kai Heitmann, HL7 Deutschland
    • Prof. Dr. Markus Feufel, Technical University Berlin

    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 analytic background (statistics, epidemiology), basic computing skills

    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

    Flyer: To download the flyer please click here.

    Advanced Epidemiologic Methods: Mastering R for Epidemiologic Research, 2.9. - .6.9.2019

    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-requisits:

    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

    To download the flyer, please click here

    Intensive Short Courses 2018

    Advanced Epidemiologic Methods – causal research & prediction modelling, 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 30 450 570 816

    Email

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

    Date: 12.4.-14.4.2018

    Lecturer:

    • Julie E. Buring, ScD, Professor of Medicine, Harvard Medical School
    • Pamela Marie Rist, Sc.D. Instructor, Harvard Medical School

    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. The format will include lectures, group exercises, and seminars.

    Prerequisites:

    • Basic knowledge of epidemiology
    • Basic medical knowledge

    Fees:

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

    ECTS: 1,25

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

    Epidemiology II, Prof. Dr. Andreas Stang, February - March 2018

    Course is in German. Please look at the German website for more details.