Biostatistics & Epidemiological Data Analysis using R (Wintersemester 2020/2021)
Dozent: Dr. rer. nat. Stefan Konigorski
(Digital Health - Machine Learning)
Website zum Kurs:
- Semesterwochenstunden: 4
- ECTS: 6
- Einschreibefrist: 01.10. - 20.11.2020
- Lehrform: Lecture & Exercise
- Belegungsart: Wahlpflichtmodul
- Lehrsprache: Englisch
Studiengänge & Module
This course teaches (i) basic epidemiological concepts and (ii) biostatistical methods and their application for data analysis of large epidemiological datasets using the statistical software R (www.r-project.org) and the graphical interface RStudio (www.rstudio.com). To this aim, the class starts with an introduction to R and RStudio. R Markdown will be used as a tool for documentation and reporting of the analysis results. Next, the class covers data processing steps and introduces epidemiological study designs as well as theoretical and practical aspects of basic and more advanced biostatistical methods. In addition to classical biostatistical approaches such as linear and linear mixed models, newer methods how to deal with missing values, how to perform meta analyses, and for causal inference will be discussed and applied.
- Introduction to R, RStudio
- Documentation and report writing using R Markdown
- Data setup: create, import, export datasets in R
- Format datasets in R: transform variables and manipulate datasets
- Descriptive statistics
- Tables and graphics to visualize data and results
- Epidemiological study designs and study planning
- Introduction to statistical parameter estimation and hypothesis testing
- Statistical methods for dealing with missing values
- Linear and logistic regression models
- Linear mixed models for the analysis of clustered and longitudinal data
- Meta analysis
- Survival analysis
- Statistical methods for causal inference
At the end of the course, the students will be able to
- understand the main concepts of basic and more advanced biostatistical methods and select appropriate methods for data analysis of epidemiological studies
- import and manipulate datasets in R for statistical analysis
- perform a data analysis in R considering measurement error and missing values
- document the analysis and report the results using R Markdown.
- Laptop with R and RStudio installation.
- While the class is self-contained, any previous exposure to programming, data analysis, and statistics is helpful.
Lern- und Lehrformen
- Lectures (via zoom) with interactive practical exercises in R
- Video snippets (provided asynchronously) with additional information on the lecture content
- Tutorials with discussion of homework
- Condition for admission to final exam: Hand in solutions to 9 of the 11 weekly assignments
- Final grade: Open book take home final exam
- Lecture with exercises: Thursdays 15:15 - 18:30 CET
- Tutorial with discussion of homework: Tuesdays 17:00 - 18:30 CET
- See time table above
- In the first tutorial on November 3, 2020, problems with installing or setting up R, RStudio, or other formal/technical questions can be clarified. This is possible between 17:00 - 19:00 via zoom. THIS WAS UPDATED TO ZOOM ONLY DUE TO THE CURRENT CORONA SITUATION.
- The lectures on December 17, 2020 and February 11, 2021 can be attended either in person in room H 2.57/58 at the HPI or via zoom.
- All other lectures and tutorials will be zoom only.
How to get access to the course
- For obtaining the recurring zoom links, please register for the course in moodle, where the link will be posted, or write an email to Stefan Konigorski.