Generalised Linear Mixed Models
Mixed models have become increasingly popular, as they have many practical applications. However, the traditional linear mixed model with normally distributed errors may not always be appropriate for modelling discrete response variables, such as binary data and counts. Typically these types of responses are analysed using generalised linear models such as logistic regression and Poisson regression.
Commonly-used generalised linear models will be extended to deal with multiple error structures, using a variety of scientific examples, including medical applications, such as investigating the presence or absence of adverse events collected in a multi-centre clinical trial.
The emphasis will be on practical understanding, although an outline of the theory will be presented. Practical examples will be used to illustrate the methods, and participants will have the opportunity to fit and interpret models themselves in hands-on computer practicals.
Who Should Attend?
Statisticians who are already familiar with linear mixed models. It will be assumed that participants are regular SAS users, and have a working knowledge of generalised linear models.
How You Will Benefit
You will learn how to formulate generalised linear models with fixed and random effects for a range of situations, and how to fit and interpret them.
What Do We Cover?
Practical work will be done in: SAS
Note: For practical work, participants must bring their own laptop with a fully licensed version of the software.
Related Courses: Linear Mixed Models