Advanced Epidemiologic Methods: Mastering R for Epidemiologic Research

ASYNCHRONOUS ONLINE COURSES, 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.

There will be two "Advanced Epidemiologic Methods: Mastering R for Epidemiologic Research" courses this Summer:

• 11 - 19 April 2022 asynchronous online course
• 22 - 26 August 2022 asynchronous online course. See online course details below.

Lecturer: Malcolm Barrett, PhD
Malcolm Barrett is a data scientist and an epidemiologist. During his Ph.D., he studied vision loss and quality of life and has since worked in the private sector, including Teladoc Health and Apple. He regularly contributes to open source software, including favorite community projects like usethis, ggplot2, 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, improve code readability, and 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 problems using linear and logistic regression.
• Advanced participants will also have the option to explore additional topics in R Markdown, Shiny, and code pipelines.

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 - April course:

• 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, April 8^th and will remain available through Wednesday, April 27^th, 2022 (we have allowed for an extra two days because of the Easter holidays). An office hour to help with access and setup will be available on Friday, April 8^th.
• Live office hour support will be provided every day between Monday, April 11^th and Thursday, April 14^th and on April 19^th from 4-5pm (Berlin time) by the Instructor, Malcolm Barrett (based in Los Angeles) as well as by a TA earlier in the day. 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 Wednesday, April 20^th, 2022.
• Students may opt to complete the course over the course of two weeks. However, only reduced office hour support will be available in the second week. The last opportunity for students to hand in their proof of achievement is Wednesday, April 27^th, 2022.
• Course material will be available via Blackboard (guest access to Blackboard will be provided before the beginning of the course)
• 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.

About the online format - August course:

• 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 19^th and will remain available through Friday, August 26^th, 2022. An office hour to help with access and setup will be availableon Friday, August 19^th.
• Live office hour support will be provided every day between Monday, August 22nd and Friday, August 26^th (Berlin time) by the Instructor, Malcolm Barrett (based in Los Angeles) as well as by a TA earlier in the day. 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 Friday, August 26^th, 2022.
• Students may opt to complete the course over the course of two weeks. However, only reduced office hour support will be available in the second week. The last opportunity for students to hand in their proof of achievement is Friday, September 2nd, 2022.
• Course material will be available via Blackboard (guest access to Blackboard will be provided before the beginning of the course)
• 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 (proof of enrollment required), 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

Partly asynchronous online course, 30 (virtual) seats

5 - 9 September 2022

Learning objectives
The aim of this introductory course is to provide the students with an overview of important areas of medical informatics. This will enhance the students’ abilities to assess and work with medical informatics technologies. The course will introduce the students to information systems and standards in health care as well as in the medical research context. The students will further learn about the most relevant data processing methods as well as data integration and data sharing approaches. The course content also covers further topics of practical relevance, such as information security and data protection, software development methods and open science approaches in medicine. Graduates of this course should be able to assess opportunities and risks presented by data-driven approaches in healthcare and medical research.

Format

• Asynchronous lectures and assignments + live exercises and office hours.
• The course consists of teaching units that are a combination of digital lectures and hands-on exercises (not graded) plus four graded assignments. Lectures will be pre-recorded, while exercises will be held live. Additional instructor/TA support will be available via virtual office hours.
• The course starts on September 5th, 2022 and consists of 25 teaching units of material organized into 5 days. We encourage students to work through these units within this schedule, as we will offer live exercises and (optional) office hour support via MS Teams throughout these 5 days.
• Proof of achievement: To earn the 2.5 ECTS credit points, students will need to complete and submit four assignments on or before Sept. 23rd, 2022. We will provide instructions on how to submit them during the lectures. To make things more flexible, we are allowing submissions of the assignments up to 3 weeks from the course start date (Note: the live office hour support only runs the first week! Please plan accordingly.)
  If you only want a certificate of participation, you just need to submit all assignments showing reasonable effort (i.e., at least 20 points to “pass”).
• Course material will be available via Blackboard (guest access to Blackboard will be provided before the beginning of the course).

Link Timetable

Optional live office hours
The course instructor and TAs will offer optional interactive office hours via MS Teams each day over the 5-day course period at 4pm-5pm. The link will be provided via “Blackboard”.

Course Information:

• Course language: English
• ECTS points: 2,5
• Course Fee: 425 € for students (proof of enrollment required), 625 € for other participants

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

Tel. +49 30 450 570 812

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

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

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

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