Modern Methods in Data Analysis (online)

The course will begin with an introduction to likelihood theory, using simple examples and a minimum of mathematics. You will then move to learning about the most important regression models used in medical research. These include logistic regression, Poisson regression, analysis of event history data, and the Cox proportional hazards regression model. In addition, you will become familiar with model validation and regression diagnostics, as well as with the basic principles of re-sampling methods and longitudinal data analysis.

The course is aimed at professionals who are interested in to learn more about statistics for medical research. However, a medical education is not a requirement to successfully participate in this course.

By the end of the course, you should be able to:

• explain the principles of the likelihood theory and maximum likelihood methods
• explain the principles of the following statistical analysis techniques: Logistic regression analysis, Poisson regression analysis, Analysis of event history data, including the Cox proportional hazards regression model
• explain model validation and regression diagnostics
• describe the basic principles of longitudinal data analysis
• apply the above-mentioned techniques using common statistical packages (e.g. SPSS or R)
• name the situations in which these techniques can be applied and the conditions that should be met to obtain reliable results using these techniques
• explain and interpret the results obtained with these techniques, and apply these results in practice (e.g. to answer a research question)

Our courses are aimed at clinical researchers, nurses, general practitioners, and other health professionals who want to improve their skills in epidemiology, statistics and (clinical) research.

9 weeks, 14 hours per week

Exam: 31 October 2025, 14:15 – 16:15 CET.
Re-examination: 19 December 2025

Exams are online using online proctoring.

Note: these times might be subject to change. The exact time and place will be announced as soon as possible in the learning environment and any changes announced there will overrule the information here.

The exam is not compulsory. However, if you want to receive the Course Certificate and the credits, it is obligatory to take the exam.

It might be that, due to a force majeure situation, you cannot be present during the first exam moment. Participants should contact the MSc Epidemiology through MSc-Epidemiology@umcutrecht.nl

Even though you can manage your own time our courses are not intended as individual education. We offer personalized online learning with lots of interaction with peer students, the E-moderator and lecturers. Flexibility from students, a positive attitude towards teachers and peers and the willingness to learn together and help each other is invaluable to our courses. To experience maximum interaction, we advise you to log on several times per week.

Note that the starting dates of courses, interim deadlines, and dates of exams are fixed, but you can choose when and where you want to watch web lectures and work on assignments. The e-moderator of the course will inform you about the beginning of the course and about deadlines during the course.

The average required study workload for the courses of MSc Epidemiology Postgraduate Online is 14 hours per week. You will need this time to study, to keep up with the assignments and course material.

To enroll in this course, you need:

• A BSc degree
• Access to the program R
• Sufficient understanding of statistics and data analysis. Elevate courses offering this knowledge include Introduction to Statistics and Classical Methods in Data Analysis
• Sufficient proficiency in English reading and writing

To enroll in this course, you need:

• A BSc degree
• Access to the program R
• Sufficient understanding of statistics and data analysis. Elevate courses offering this knowledge include Introduction to Statistics and Classical Methods in Data Analysis
• Sufficient proficiency in English reading and writing

Please note that this course is part of an existing program within the Graduate School of Life Sciences. Tuitition fees may alter during the year.