IT-Systems Engineering

Biostatistics & Epidemiological Data Analysis using R 2023-24

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.

General Information

  • Lecturer: Dr. Stefan Konigorski (stefan.konigorski@hpi.de)
  • SWS: 4+2
  • ECTS: 6
  • Graded: Yes
  • Enrolment Deadline: 01.10. - 31.10.2023
  • Enrolment Type: Compulsory Elective Module
  • Course Language: English 

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 & 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 the data analysis in R considering measurement error and missing values
  • document the analysis and report the results using R Markdown.

Teaching form

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

Prerequisites

Laptop with R and RStudio installation:

Condition for admission to final exam

  • Hand in solutions to 9 of the 11 weekly assignments

Final grade

Important

  • Please enrol to the course here on Moodle, or send an email to Stefan Konigorski in case of any problems


ePortfolio: Nein