Intensive Short Courses

Mastering R for Epidemiologic Research 

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

Lecturer: Malcolm Barrett, PhD

Malcolm Barrett is a data scientist, epidemiologist, and research software engineer at Stanford University. During his PhD, he studied vision loss and quality of life and has since worked at Posit (formerly known as RStudio) and in the private sector, including Teladoc Health and Apple. He develops and contributes to open-source software related to causal inference, reproducible research, R package development, and more.

​​​​​Course Information:

• Format: asynchronous online course, 50 (virtual) seats
• Dates: Full course materials will be available on April 27. The course can be completed in one week (full-time) or flexibly over up to four weeks.
• ECTS: 3
• Course Language: English
• Course Fees:
• € 510 reduced fee (students, Charité researchers, BSPH alumni)
• € 750 regular fee 

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, improve code readability, and make analyses more reproducible.
• Written dynamic documents using best practices for reproducible research using Quarto. Quarto intertwines code and text so that reports and articles are fully reproducible and exportable to PDF, HTML, Word, and more. Quarto also has excellent support for citation management and formatting for journals.
• Modeled simple statistical problems using linear and logistic regression.

Prerequisites:
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) or work through the Posit Primers (https://posit.cloud/learn/primers). 

Materials:
R is a free open-source programming language and tool for statistics. For the hands-on exercises, you can either use Posit 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/). 

All course materials will be made available through a course website and can be kept after the course.

About the online format:
The course will be entirely asynchronous, and participants can work entirely at their own pace. The content is equivalent to a one-week, 40-hour, full-time course. 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.

• The course consists of 40 teaching units of material organized into 5 full workdays (8 hours of student investment time per workday).
• Course material will be made available on April 27. An office hour to help with access and setup will be available in the week before the course starts. 
• Participants who can devote 5 full work days to the class should be able to complete all the required work by May 4. Participants may also opt to complete the course over up to four weeks. The last opportunity for students to hand in their proof of achievement is May 26.
• Live office hour support via MS Teams will be provided on May 4, 11, and 18 from 4-5 pm (Berlin time) and on May 20 from 4:30-5:30 pm by the instructor, Malcolm Barrett. Additional office hours will be provided by a teaching assistant. The office hours are optional.

Proof of achievement:
Participants 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., achieving at least 50% to pass).

Please register using the following link: https://forms.office.com/e/XQaX1UEN0a

Advanced Epidemiologic Methods

The course is designed for researchers, public health professionals, epidemiologists, and clinicians familiar with advanced epidemiologic knowledge, algebra, and statistical computing. 

Lecturer: Matthew Fox, DSc, MPH 

Matthew Fox is a Professor in the Departments of Epidemiology and Global Health at Boston University. Dr. Fox joined Boston University in 1999. His research interests include treatment outcomes in HIV-treatment programs, infectious disease epidemiology (with specific interests in HIV and pneumonia), and epidemiologic methods. Dr. Fox works on ways to improve retention in HIV-care programs in South Africa from the time of testing HIV-positive through long-term treatment. As part of this work, he is involved in analyses to assess the impact of changes in South Africa’s National Treatment Guidelines for HIV. Dr. Fox also does research on quantitative bias analysis and co-authored a book on these methods, Applying Quantitative Bias Analysis to Epidemiologic Data. He is also the host of a public health journal club podcast called Free Associations, designed to help people stay current in the public health literature and think critically about the quality of research studies and a podcast on Epidemiologic Methods called SERious Epi. He currently teaches a third-level epidemiologic methods class, Advanced Epidemiology, as well as two other doctoral-level epidemiologic methods courses. Dr. Fox is a graduate of the Boston University School of Public Health with a master’s degree in epidemiology and biostatistics and a doctorate in epidemiology.

Course Information:

• Format: in-person course (online participation is not possible), 40 seats
• Dates: August 17 - 20, 9:00 am - 4 pm, Preliminary Schedule
• Location: Campus Charité Mitte
• ECTS: 2.5
• Course Language: English
• Course Fees:
• € 425 reduced fee (students, Charité researchers, BSPH alumni)
• € 625 regular fee

Course Description and Learning Objectives
The intention of this intensive short course is to strengthen the methodological skills of the research community. At the end of the week, participants should be able to:

• Use the sufficient cause model, counterfactual susceptibility type model, and causal graphs to assist with the design and analysis of epidemiologic studies.
• 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.

Prerequisites:
A course in introductory epidemiology and statistics. Coursework in intermediate epidemiology and biostatistics is strongly recommended.

Materials:
Lecture slides and exercises will be made available for download during the course.

Proof of achievement:
Participants will be required to complete 4 quizzes consisting of short answer, true/false, and calculation questions 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 50% to pass).

Please register using the following link: https://forms.office.com/e/XQaX1UEN0a 

More information coming soon!