# Data Analysis using R, 1 - Statistical Epidemiology (Wintersemester 2019/2020)

Lecturer: Dr. rer. nat. Stefan Konigorski (Digital Health & Machine Learning)
Course Website: https://moodle2.uni-potsdam.de/course/index.php?categoryid=1532

## General Information

• Weekly Hours: 4
• Credits: 6
• Enrolment Deadline: 01.10. - 30.10.2019
• Teaching Form: Lecture / Seminar
• Enrolment Type: Compulsory Elective Module
• Course Language: English
• Maximum number of participants: 30

## Programs & Modules

IT-Systems Engineering MA
• OSIS-Konzepte und Methoden
• OSIS-Spezialisierung
• OSIS-Techniken und Werkzeuge
• ITSE-Analyse
• ITSE-Entwurf
• ITSE-Konstruktion
• ITSE-Maintenance
Data Engineering MA
Digital Health MA

## Description

This course teaches 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 regression and regression models for binary and count data
• Linear mixed models for the analysis of clustered and longitudinal data
• Meta analysis
• Survival analysis
• Optional: 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

## Requirements

Already at first appointment bring your own laptop with R and RStudio installation.

Needed installations:

Help for the installation can be found e.g. at http://r-tutorial.nl/, or in the first two sessions of Prof. Arnrich's Bridgemodule course (Fundamentals of Programming, October 14 + 15)).

## Learning

Lectures, practical exercises, group exercises

## Examination

Condition for admission to final exam:
(i) Hand in solutions to 10 of the 12 weekly assignments
(ii) Pass midterm exam