Hasso-Plattner-InstitutSDG am HPI
Hasso-Plattner-InstitutDSG am HPI
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Biostatistics & Epidemiological Data Analysis using R (Wintersemester 2022/2023)

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

General Information

  • Weekly Hours: 4
  • Credits: 6
  • Graded: yes
  • Enrolment Deadline: 01.10.2022 - 31.10.2022
  • Examination time §9 (4) BAMA-O: 08.02.2023
  • Teaching Form: Lecture / Exercise
  • Enrolment Type: Compulsory Elective Module
  • Course Language: English
  • Maximum number of participants: 60

Programs, Module Groups & Modules

IT-Systems Engineering MA
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-K Konzepte und Methoden
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-T Techniken und Werkzeuge
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-S Spezialisierung
Data Engineering MA
Digital Health MA
Software Systems Engineering MA

Description

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.

Content:

  • 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.

Requirements

  • 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.

Learning

  • Lectures (via zoom) with interactive practical exercises in R
  • Tutorials with discussion of homework

Examination

  • 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

Dates

Lecture (online):
Wednesday Block (3:15pm-6:30pm)

Excercises:
Tuesday 5pm-6:30pm

Link to Moodle where you can subscribe, find the zoom link and all other materials and information about the course:
https://moodle2.uni-potsdam.de/course/view.php?id=34607

Timetable

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