Epidemiology and Research Design

This course is designed to provide students with essential concepts and skills in epidemiology. Students will have the chance to familiarize themselves with different study designs (e.g. cohort study, case-control study), gain insights into statistical methods employed in epidemiological settings, enhance their abilities to connect health data with underlying phenomena, and develop grounded conclusions. On the completion of this course, the students will feel comfortable with subsequent advanced courses and be better prepared for their real-world projects.


Prerequisites: Good knowledge of calculus, algebra, and health statistics. Students with little background in statistics are encouraged to take Quantitative Methods course in advance or simultaneously.

Type of examination: Written Exam

Course coordinator: Prof. Dr. Ulrich Mansmann

When: Winter Semester

If you are interested, you can register with a form that you will receive from the Ph.D. program office: phd@ibe.med.uni-muenchen.dephd@ibe.med.uni-muenchen.de

Detailed information about the courses can be obtained from the MSc team: msc@ibe.med.uni-muenchen.de

Course Structure

Lectures are prepared and delivered by Professor Ulrich Mansmann, where students are exposed to a broad range of topics, including risk measures, confounding and bias, epidemiological techniques, study designs, guidelines, etc.

Exercises are led by an experienced IBE instructor. Students will learn to apply theoretical methods by solving problems, and will deepen their comprehension of the concepts introduced during the lectures.

Tutorials offered by a senior student provides briefings of the lecture content and additional exercises for recitation. Students will also benefit from the prior study experience from their tutor.

Paper Presentation gives a chance for students to convey their understandings of epidemiological literature as a group to their fellow students.

R course teaches the most popular, open-source programming language in epidemiological data analysis. It focuses on data operations, the realization of descriptive methods and regression models, and the interpretation of R outputs. Students will further practice their programming skills in an R project.