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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 statistical package 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)

The online course is a 9 week part-time course with a study load of 14 hrs/w. Web lectures, group assignments and group discussions are the learning methods that will be used. This course is offered based on asynchronous learning, hence there are no plenary sessions. There are interim deadlines and the course ends with an exam.

Online learning with interaction and support. 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.

Didactic work methods are web lectures, group assignments and group discussions are the learning methods that will be used. There are interim deadlines and the course ends with an exam.

Course material

You need to have access to and working knowledge of the freeware statistical program R.

Course fee

We offer 10 places for free for PhD candidates registered with the Graduate School of Life Sciences via MyPhD (except for PhD candidates in the Epidemiology programme). Be quick, these 10 places tend to be taken soon. 

Group size

8 to 30 participants

Number of credits

4.5 EC

Course certificate

Participants will receive a certificate after finishing the course, but only when all the necessary requirements are met:

• Participants should adhere to all the course deadlines and finish the learning units in time
• Participants should pass the exam with a sufficient grade i.e. not below 5.5

This course is free for GSLS PhD candidates. However: free of charge does not mean free of responsibility. Once you have signed up for a course, we expect you to attend. For every late cancellation or no-show we have had to disappoint others who would have liked to attend. This is our policy:

• You may cancel free of charge up to 4 weeks before the start of the course. After this date you can only cancel if you have a GSLS PhD candidate to replace you in the course. Send the name and contact information of your replacement to pcc@uu.nl, at least 2 working days before the start of the course;

• We expect that you actively attend the full course. You have to complete the total E-learning.

• Not meeting the above requirements means you will be charged a no-show fee (€ 583,12). We will send the invoice after the course has ended. We are unable to make any exceptions, unless you have a valid reason (i.e., illness or death in the family 1^st/2^nd degree or partner). Your supervisor has to send an e-mail to pcc@uu.nl indicating the reason.