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Archive of the Intensive Short Courses

If you would like to know which courses have taken place at the Berlin School of Public Health in recent years, you will find an overview on this page.

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Intensive Short Courses 2021

ONLINE Malcolm Barrett: Mastering R for Epidemiologic Research, August 16 - August 20, 2021

Advanced Epidemiologic Methods: Mastering R for Epidemiologic Research

ONLINE COURSE, 40 (virtual) seats

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: August 16 - August 20, 2021

Lecturer: Malcolm Barrett, PhD

Malcolm Barrett is a Clinical Research Data Scientist. 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 course, 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:

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. This course is for clinical researchers and epidemiologists wanting to gain advanced R programming skills for data manipulation and visualisation. The course does not cover the underlying statistical methods.

Materials:

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

About the online format:

  • This is an asynchronous online course. The course consists of 40 teaching units of material organized into 5 full workdays (8 units / hours of student investment time per workday).
  • Course material will be made available on Friday, August 13th and will remain available through Monday, August 20th, 2021. An office hour to help with access and setup will be available on Friday, August 13th from 12 noon to 1 pm. 
  • Live office hour support will be provided every day between Monday, August 16th and Friday, August 20th from 4-5 pm by the Instructor, Malcolm Barrett (based in Los Angeles) as well as from 12 noon -1 pm by the TA, Sebastian Hinck. The office hours are optional. 
    • Students who can devote a full work day to the class should be able to complete all the required work in the first week and can hand in their proof of achievement on Monday, August 23, 2021. 
    • Students may opt to complete the course over the course of two weeks. However, office hour support will only be available on Monday, Wednesday and Friday from 12 noon -1 pm with Sebastian Hinck in the second week. The last opportunity for students to hand in their proof of achievement is Monday, August 30th, 2021. 

  • Course material will be available via Blackboard.
  • Students will need to complete and submit 4 assignments (online) in order to receive ECTS credit. If you only want a certificate of participation, you will need to submit all assignments showing reasonable effort (i.e. at least 20 points to “pass”).
  • To maximize flexibility all around, the course will be entirely asynchronous. That is, students can work entirely at their own pace. The content is equivalent to a one week, 40 hour, full-time course with 3 ECTS. Some content will be clearly marked as “optional” for those who want to take a “deeper dive”, seek additional examples and/or want to learn more advanced programming skills. Those who are less advanced can skip these sections.

Course Information:

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

Registration:

Please use the registration form or send an e-mail to Tanja Te Gude

Tel. +49 30 450 570 812

ONLINE Malcolm Barrett: Mastering R for Epidemiologic Research, March 08 - March 12, 2021

Advanced Epidemiologic Methods: Mastering R for Epidemiologic Research

ASYNCHRONOUS ONLINE COURSE, 40 (virtual) seats

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: March 08 - March 12, 2021

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 course, 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 is a free open source programming language and tool for statistics. For the hands-on exercises, you can either use RStudio Cloud (advantage: “plug-and-play”, no need to install anything) or download and install the most recent version of R (https://www.r-project.org/) as well as RStudio (https://rstudio.com/products/rstudio/download/).

About the online format:

  • This is an asynchronous online course. The course consists of 40 teaching units of material organized into 5 full workdays (8 units / hours of student investment time per workday).
  • Course material will be made available on Friday, March 5th and will remain available through Monday, March 22nd, 2021. An office hour to help with access and setup will be available on Friday, March 5th from 4-5pm. 
  • Live office hour support will be provided every day between Monday, March 8th and Friday, March 12th from 4-5pm by the Instructor, Malcolm Barrett (based in Los Angeles) as well as from 10-11am by the TA, Sebastian Hinck. The office hours are optional. 
    • Students who can devote a full work day to the class should be able to complete all the required work in the first week and can hand in their proof of achievement on Monday, March 15, 2021. 
    • Students may opt to complete the course over the course of two weeks. However, office hour support will only be available on March 15th, 17th and 19th from 10-11am with Sebastian Hinck in the second week. The last opportunity for students to hand in their proof of achievement is Monday, March 22, 2021. 

  • Course material will be available via Blackboard.
  • Students will need to complete and submit 4 assignments (online) in order to recieve ECTS credit. If you only want a certificate of participation, you will need to submit all assignments showing reasonable effort (i.e. at least 20 points to “pass”).
  • To maximize flexibility all around, the course will be entirely asynchronous. That is, students can work entirely at their own pace. The content is equivalent to a one week, 40 hour, full-time course with 3 ECTS. Some content will be clearly marked as “optional” for those who want to take a “deeper dive”, seek additional examples and/or want to learn more advanced programming skills. Those who are less advanced can skip these sections.

Course Information:

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

Registration:

Please use the registration form or send an e-mail to Tanja Te Gude

Tel. +49 30 450 570 812

Intensive Short Courses 2020

ONLINE Malcolm Barrett: Mastering R for Epidemiologic Research, August 31 - Sept. 11, 2020

Advanced Epidemiologic Methods: Mastering R for Epidemiologic Research

ONLINE COURSE, 50 (virtual) seats

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: August 31 - Sept. 11, 2020

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 course, 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.

About the online format:

  • The course starts Monday August 31st and will run for 2 weeks with instructor/TA support
  • Course material will be linked from Blackboard
  • Students will need to complete and submit 4 assignments (online) for credit. We will allow submissions of the assignments up to 3 weeks from the course start date. Students can therefore work through the course at an even slower pace (also on the weekends, if desired), just being aware that the office hour support will only run the first two weeks. For those who still prefer the compact format, it is of course also possible to finish the course within 1 week.
  • To maximize flexibility all around, the course will be entirely asynchronous. That is, students can work entirely at their own pace. The content will be equivalent to the 1 week, full-time course (3 ECTS). Some content will be clearly marked as “optional” for those who want to take a “deeper dive”, seek additional examples and/or want to learn more advanced programming skills. Those who have less time could skip these sections.
  • During the 2-week period starting Aug 31st, on weekdays, Malcolm Barrett would hold regular office hours (via Zoom) in the early morning Los Angeles time, end of work day Berlin time (exact time TBD). Teaching assistants (Berlin-based) would offer office hours earlier in the day.

Course Information:

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

Registration:

Please use the registration form or send an e-mail to Tanja Te Gude

Tel. +49 30 450 570 812

ONLINE Gesundheitspädagogik, 24.08.- 28.08. und 17.09.- 19.09.2020 (2 Blöcke)

This course is in German. Please switch to the German website for more information.

Miguel Hernán / Katalin Gémes: Causal inference - Learning what works, February 19 - 20, 2020

FULLY BOOKED

Causal inference: Learning what works

Lecturers: Miguel Hernán (Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health), Katalin Gemes (Karolinska Institutet, Sweden)

Biography: Miguel Hernán conducts research to learn what works to improve human health. Together with his collaborators, he designs analyses of healthcare databases, epidemiologic studies, and randomized trials. Miguel teaches clinical data science at the Harvard Medical School, clinical epidemiology at the Harvard-MIT Division of Health Sciences and Technology, and causal inference methodology at the Harvard T.H. Chan School of Public Health, where he is the Kolokotrones Professor of Biostatistics and Epidemiology. His edX course Causal Diagrams and his book Causal Inference, co-authored with James Robins, are freely available online and widely used for the training of researchers. Miguel is an elected Fellow of the American Association for the Advancement of Science and of the American Statistical Association, an Editor of Epidemiology, and past Associate Editor of Biometrics, American Journal of Epidemiology, and the Journal of the American Statistical Association.

Katalin Gémes is a postdoctoral researcher at the Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. She defended her PhD work in the field of Public Health Epidemiology. She currently works with combining data from observational studies and routinely collected administrative information to compare the effectiveness of different hypothetical interventions on diet and alcohol consumption on the risk of cardiovascular diseases.

Course objectives: To learn how to determine “what works” using data from observational and randomized studies 

Description: The course introduces students to a general framework for the assessment of comparative effectiveness and safety, with an emphasis of the use of routinely collected data in healthcare settings. The framework relies on the specification and emulation of a hypothetical randomized trial: the target trial. The course explores key challenges for causal inference and critically reviews methods proposed to overcome those challenges. The methods are presented in the context of several case studies for cancer, cardiovascular, and renal diseases.

Learning Objectives: After successful completion of this course, students will be able to:

  • Formulate sufficiently well-defined causal questions for comparative effectiveness research
  • Specify the protocol of the target trial
  • Design analyses of observational data that emulate the protocol of the target trial
  • Identify key assumptions for a correct emulation of the target trial

Pre-course reading: Chapters 1-3 of the book Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC, forthcoming. The book can be downloaded (for free) from http://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/

ECTS:

1.25 ECTS 

Fees:

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

 

Intensive Short Courses 2019

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

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: 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

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.

(Only in German) Gesundheitspädagogik, 29.07.- 02.08. und 29.08.- 31.08.2019 (2 Blöcke)

Please switch to the German Website for more information.

How to publish a research paper in a major biomedical journal, 22.05. - 24.05.2019

University of Zagreb – School of Medicine, Andrija Štampar School of Public Health and Charité – Universitätsmedizin Berlin, Institute of Public Health in affiliation with the Berlin School of Public Health are organising a postgraduate continuing medical education course: "How to publish a research paper in a major biomedical journal".

Date: 22.05. - 24.05.2019

Lecturer:
Charité - Universitätsmedizin Berlin:
Tobias Kurth (MD, ScD), Professor, Director of the Institute of Public Health
Marco Piccininni, MSc Statistician, PhD Candidate
Jessica Rohmann, MSc Epidemiologist, PhD Candidate

Andrija Štampar School of Public Health, University of Zagreb
Kristina Fišter (MD, MSc, DSc), Assistant Professor, Andrija Štampar School of Public Health
Pero Hrabač (MD), Assistant, PhD Candidate
Danko Relić (MD), Assistant, PhD Candidate

About the Short Course

Publishing visibly is closely linked with success in obtaining grants, yet competition for space is fierce, particularly in major biomedical journals. As research methods evolve and the publishing landscape changes, continuing education in knowledge and skills needed to produce quality research articles is necessary. This is all the more relevant as capacity is strengthened for health data sciences and decisions are increasingly made based on solid evidence.

Participants will refresh the existing and gain new knowledge and skills needed to produce a high-quality research paper. This includes posing an important and relevant research question, choosing an appropriate study design, writing a research protocol, using electronic data capture solutions, conducting data analysis using statistical program R, interpretation of the findings, searching biomedical literature, writing a research paper and referencing previous work, as well as submitting the paper to a top biomedical journal.

The course is designed for medical doctors of all specialties as well as other biomedical and health professionals.

Language

English

Credits

2.4 ECTS points (University of Zagreb School of Medicine)

To Apply

Applications for the course are accepted at this link.

Course Fees

500 € (includes lunches and refreshments during breaks).

Contact

For all queries please get in touch with Mrs Andrea Brozović, Course Administrator
Phone: +385 1 45 90 107, Email: publish(at)mef.hr

Link

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.