IPH-Lecture (John Gill, University of British Columbia)
Oct
23
4:00 PM16:00

IPH-Lecture (John Gill, University of British Columbia)

Understanding and communicating risk of rare but serious health complications – an example from living kidney donation

John Gill is Professor of Medicine at the University of British Columbia in Vancouver, Canada. He research interests include clinical research, clinical trials, health policy and health services research related to kidney transplantation. John received the Established Clinical Investigator Award from the American Society of Transplantation in 2017, is Deputy Editor of the American Journal of Transplantation, Officer of the American Society of Transplantation, and is supported by a Foundation Award from The Canadian Institutes of Health Research.

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Propensity Score Analysis: Theoretical & Practical Considerations
Jan
21
to Jan 24

Propensity Score Analysis: Theoretical & Practical Considerations

  • Harvard T.H. Chan School of Public Health (map)
  • Google Calendar ICS

This course introduces basic and advanced theory underlying propensity score analyses and provides practical insights into the conduct of studies employing the method. Course readings will include propensity score theory as well as applications. Lectures are complemented by computer lab sessions devoted to the mechanics of estimating and using the propensity score as a tool to control for confounding in observational research. Students should have knowledge in multivariable modeling approaches. A course project will involve the application of propensity scores to a data set or the review of a related, published paper.Course Activities: Lectures, readings, homeworks, computer labs, participation, project.

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18th Biennial European Conference
Jun
14
to Jun 16

18th Biennial European Conference

  • Langenbeck Virchow Haus (map)
  • Google Calendar ICS

Linking Research to Evidence-Based Action for Patients, Providers and Policy Decision Makers


Conference Co-Chairs: Beate Jahn, PhD, Silke Siebert, MD, Tobias Kurth, MD, MSc, ScD, and Uwe Siebert, MD, MPH, MSc, ScD

Call for Abstracts & Short Courses opens December 2019. 

Registration opens April 2020.

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Intensive Short Course: Mastering R for Epidemiologic Research
Sep
2
to Sep 6

Intensive Short Course: Mastering R for Epidemiologic Research

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

Contact: tanja.tegude@charite.de

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Intensive Short Course: Applied Digital Health
Aug
19
to Aug 23

Intensive Short Course: Applied Digital Health

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

Contact: tanja.tegude@charite.de

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Intensive Short Course: Advanced Epidemiologic Methods (Matt Fox)
Aug
12
to Aug 16

Intensive Short Course: Advanced Epidemiologic Methods (Matt Fox)

Lecturer:

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

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

Contact: tanja.tegude@charite.de

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Intensive Short Course: Medical Informatics
Aug
5
to Aug 9

Intensive Short Course: Medical Informatics

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

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Global Health Conference
Jun
28
10:00 AM10:00

Global Health Conference

Chances and Implications of Digitalization in Healthcare in Germany and France

In partnership with the scientific service of the Embassy of France in Berlin

Panel-discussion: „How is digitalization changing our healthcare system“ with: Peuker (CIO Charité), Stöckemann (CEO Peppermint Venture Partners), Tsimpoulis (Managing Director Doctolib)

The advancement of digitization is the central prerequisite for the successful development of our healthcare system”, according to the website of the German Ministry of Health.

How far did Germany and France get in 2019? How do we as patients, caregivers, scientists and entrepreneurs experience the upheaval and the possibilities that digitalization and new technologies offer?

How do roles and responsibilities for both patients and caregivers change and how do the stakeholders manage this transformation? And is everybody sufficiently aware of the ethical questions which come along when robots and algorithms replace human beings? And after all, will this improve healthcare for the individual?

With this year’s conference, hosted by the Centre Virchow-Villermé for Public Health Paris-Berlin, we invite you to gain an insight into the developments of digitalization in different areas of the healthcare system in Germany and France and to discuss opportunities and implications of digitalization in healthcare on different levels.

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EPI 271, propensity score methods
Jan
22
to Jan 25

EPI 271, propensity score methods

  • Harvard T.H. Chan School of Public Health (map)
  • Google Calendar ICS

Description
This course introduces basic and advanced theory underlying propensity score analyses and provides practical insights into the conduct of studies employing the method. Course readings will include propensity score theory as well as applications. Lectures are complemented by computer lab sessions devoted to the mechanics of estimating and using the propensity score as a tool to control for confounding in observational research. Students should have knowledge in multivariable modeling approaches. A course project will involve the application of propensity scores to a data set or the review of a related, published paper.Course Activities: Lectures, readings, homeworks, computer labs, participation, project. HSPH Course Prerequisite(s): EPI204 or EPI236 or EPI 522 or BST210 or BST213; may not be taken concurrently.

Prerequisite(s)
HSPH Prerequisite: EPI204 or EPI236 or EPI 522 or BST210 or BST213; may not be taken concurrently. Students outside of HSPH must request instructor permission to enroll in this course

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Advanced Epidemiologic Methods – causal research and prediction modeling
Aug
20
to Aug 24

Advanced Epidemiologic Methods – causal research and prediction modeling

  • Charité Campus Virchow-Klinikum (map)
  • Google Calendar ICS

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

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

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Jahresabschlussfeier der Berlin School of Public Health
Jun
8
5:00 PM17:00

Jahresabschlussfeier der Berlin School of Public Health

  • Charité Campus Mitte, Hörsaal Innere Medizin Südflügel (map)
  • Google Calendar ICS

PROGRAMM

17:00
Begrüßung
Prof. Dr. Reinhard Busse Technische Universität Berlin

17:15
Keynote-Lecture
Prof. Dr. Eva Inés Obergfell
Vizepräsidentin für Lehre und Studium der Humboldt-Universität zu Berlin

17:45
Rückblick, Ausblick, Würdigung
Dr. Nina Adelberger
Charité – Universitätsmedizin Berlin

18:00
Laudatio und Verleihung Berlin School of Public Health-Preis
Prof. Dr. Dr. Tobias Kurth
Charité – Universitätsmedizin Berlin

18:30
Get-together

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"The Complexity of Clinical Trials in the Prevention of Cardiovascular Disease: An Investigator's Perspective"
Apr
17
4:00 PM16:00

"The Complexity of Clinical Trials in the Prevention of Cardiovascular Disease: An Investigator's Perspective"

Prof Julie Buring, Prof. Dr. Dr. Tonias Kurth

Randomized clinical trials are more logistically difficult, more expensive, and need to consider more ethical issues than any other epidemiologic design strategy – yet if well designed and conducted, randomized trials can provide the highest level of assurance about the effect of the intervention itself. This lecture will address the complexity of randomized trials of prevention from an investigator’s standpoint, in the context of the evaluation of aspirin in the primary prevention of cardiovascular disease.

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Propensity Score Analysis: Theoretical & Practical Considerations
Jan
16
to Jan 19

Propensity Score Analysis: Theoretical & Practical Considerations

  • Harvard T.H. Chan School of Public Health (map)
  • Google Calendar ICS

This course introduces basic and advanced theory underlying propensity score analyses and provides practical insights into the conduct of studies employing the method. Course readings will include propensity score theory as well as applications. Lectures are complemented by computer lab sessions devoted to the mechanics of estimating and using the propensity score as a tool to control for confounding in observational research. Students should have knowledge in multivariable modeling approaches. A course project will involve the application of propensity scores to a data set or the review of a related, published paper.Course Activities: Lectures, readings, homeworks, computer labs, participation, project.

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