Hasso-Plattner-Institut25 Jahre HPI
Hasso-Plattner-Institut25 Jahre HPI

Biostatistics & Epidemiological Data Analysis using R (Wintersemester 2022/2023)

Dozent: Dr. rer. nat. Stefan Konigorski (Digital Health - Machine Learning)
Website zum Kurs: https://moodle2.uni-potsdam.de/course/view.php?id=34607

Allgemeine Information

  • Semesterwochenstunden: 4
  • ECTS: 6
  • Benotet: Ja
  • Einschreibefrist: 01.10.2022 - 31.10.2022
  • Prüfungszeitpunkt §9 (4) BAMA-O: 08.02.2023
  • Lehrform: Vorlesung / Übung
  • Belegungsart: Wahlpflichtmodul
  • Lehrsprache: Englisch
  • Maximale Teilnehmerzahl: 60

Studiengänge, Modulgruppen & Module

IT-Systems Engineering MA
Data Engineering MA
Digital Health MA
  • Digital Health
    • HPI-DH-DS Data Science for Digital Health
  • SCAD: Scalable Computing and Algorithms for Digital Health
    • HPI-SCAD-C Concepts and Methods
  • SCAD: Scalable Computing and Algorithms for Digital Health
    • HPI-SCAD-T Technologies and Tools
  • SCAD: Scalable Computing and Algorithms for Digital Health
    • HPI-SCAD-S Specialization
  • DICR: Digitalization of Clinical and Research Processes
    • HPI-DICR-C Concepts and Methods
  • DICR: Digitalization of Clinical and Research Processes
    • HPI-DICR-T Technologies and Tools
  • DICR: Digitalization of Clinical and Research Processes
    • HPI-DICR-S Specialization
  • APAD: Acquisition, Processing and Analysis of Health Data
    • HPI-APAD-C Concepts and Methods
  • APAD: Acquisition, Processing and Analysis of Health Data
    • HPI-APAD-T Technologies and Tools
  • APAD: Acquisition, Processing and Analysis of Health Data
    • HPI-APAD-S Specialization
Software Systems Engineering MA


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

Learning goals:

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 installation of R (recommended: version 4.2.1) and RStudio (recommended: RStudio-2022.07.1-554).
  • 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
  • Tutorials with discussion of homework


  • This class will also be open to students from the Icahn School of Medicine at Mount Sinai in New York.
  • All lectures and tutorials will be through zoom, with some tutorials in hybrid mode in presence at HPI. The lectures will be recorded and made available afterwards (see Moodle for details).
  • 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 (online):
Wednesday Block (3:15pm-6:30pm)

Tuesday 5pm-6:30pm

Link to Moodle where you can subscribe, find the zoom link and all other materials and information about the course: