Introduction to Generalised Linear Mixed Models using Stata
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 examples generally drawn from medical and health related fields. Specific applications, such as repeated measurements and multi-centre trials will also be considered. For example, 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. The Stata software will be used for practical work and to illustrate analyses in presentations.
Who Should Attend?
Data analysts and statisticians working in medicine, health and related areas, who wish to have a practical introduction to generalised linear mixed models. It is assumed that participants are Stata users and are familiar with the practical use of generalised linear models and linear mixed models.
How You Will Benefit
You will learn to formulate generalised linear models with both fixed and random effects for a range of practical situations, and how to fit and interpret these models.
What Do We Cover?
Practical work may be done in: Stata
Note: For practical work, participants must bring their own laptop with a fully licensed version of the software.
Related courses: Introduction to Linear Mixed Models using Stata; Introduction to Linear Mixed Models using R; Linear Mixed Models; Generalised Linear Mixed Models