Introduction to Generalised Linear Mixed Models using R

Overview

Mixed models have become increasingly popular, as they have many practical applications. However, the traditional linear mixed model with normally distributed errors is not appropriate for modelling discrete responses such as binary data and counts. Such responses are typically 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, mainly medical and health related applications, such as investigating the presence of adverse events in a 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.

Practical work will be based on the R software. Model fitting will mainly be done using the CRAN package GLMMadaptive.

Duration

2 days

Delivery Mode

All training is online and will be delivered live each day between 09:00 and 17:30 (GMT). Delivery platform: Zoom, which may be freely accessed. Questions may be asked using Zoom's chat box. Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support. During presentations, the team member who is not speaking can take questions in addition to the presenter.?

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 R users and familiar with the practical use of both 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 situations, how to fit them and how to interpret their output.

What Do We Cover?

• Review of generalised linear models and linear mixed models
• Binary and binomial outcomes: logistic regression with mixed effects
• Count outcomes: Poisson and negative binomial regression with mixed effects
• Ordered outcomes: proportional odds regression with mixed effects
• Adaptive Gauss-Hermite Quadrature fitting method; inferential procedures
• Convergence issues and solutions
• Interpretation of effects in a generalised linear mixed model and prediction
• GLMMadaptive CRAN package for fitting generalised linear mixed models.

Notes on course content:

• The GLMMadaptive package can currently only fit models where the random effects part is defined by a single grouping factor
• The course does not cover marginal or GEE type models for repeated measurements.

Software

Practical work will be done in R.

Note: For practical work, participants must download and install a number of CRAN packages in R. This must be done prior to the start of the course.