Mixed Models (online)

In the biosciences, response variables are often observed more than once per individual. This enables the researcher to study the development of the variable of interest within individuals, thereby eliminating the variation among individuals, and thus increasing the power of the design. However, since observations on the same individual are almost always correlated, special methods are needed to deal with this dependence.

Another way in which data can be dependent is when there is a hierarchical (multilevel) structure in your data, e.g. patients within hospitals, horses within farms, pupils within classrooms, etc.

Mixed models are one way of analyzing this kind of data. This statistical technique allows for the dependency of measurements in hierarchically structured data, and separately examines the effects of variables at different levels. An important part of the course will be about the use (and theory) of linear mixed effects models (LME’s).

Starting with analysis of summary statistics on each individual’s observations, this course will lead you to more advanced methods for analyzing multilevel and longitudinal data. Similarities between longitudinal data analysis and multilevel analysis will be clarified. The course will focus primarily on continuous outcome variables, but attention will also be paid to dichotomous and count data.

The theory will be presented during lectures; computer lab sessions using R will give you the opportunity to practice your skills on real data sets.

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

• understand the difference between fixed and random effects
• know when to apply a mixed model in practice
• perform mixed model analyses using statistical software R
• interpret the output of mixed model analyses in terms of the context of the research question(s)
• know the most commonly used methods for checking model appropriateness and model fit
• report the results of mixed model analyses to non-statistical investigators

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.

6 weeks, 7 hrs / week

To successfully complete this course, you need to actively participate in the discussion forums and complete the learning unit assignments, including:

• Individual and group assignments
• The completion of a quiz at the end of the first four learning units
• A final assignment: this will be a presentation of a case study by the student. The submission deadline and the date for the redo session will be announced as soon as possible.

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
• Basic programming experience in R, e.g. the ability to read in data and run a simple linear model
• To have followed at least one course in basic statistical methods up to and including simple and multiple linear regression
• Familiarity with likelihood methods (Wald, score and likelihood ratio tests) will facilitate understanding of the theoretical background.

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.