Introduction to Generalised Linear Mixed Models using Stata (online)
Date:
17/04/2024 - 18/04/2024
Organised by:
Statistical Services Centre Ltd
Presenter:
James Gallagher and Sandro Leidi
Level:
Advanced (specialised prior knowledge)
Contact:
James Gallagher
07873873617
jamesgallagher1929@gmail.com
Venue: Online
Description:
Overview of 2-day course
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 based practicals. The Stata software will be used for practical work and to illustrate analyses in presentations.
Cost
£534 (inclusive of 20% VAT)
Delivery Mode
All training is online and will be delivered live each day between 09:00 and 17:30 (GMT+1). 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. We also use Zoom meetings rather than webinars to encourage further interaction during an online course.
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?
- Review of generalised linear models for binary and count data and linear mixed models
- Binary and binomial responses: logistic model with mixed effects
- Count responses: Poisson and negative binomial model with mixed effects
- Ordered categorical responses: proportional odds model with mixed effects
- Fitting methods; convergence issues and solutions
- Inferential procedures
- Applications such as repeated measurements and multi-centre trials
- Use of dedicated Stata commands: -meglm-, -melogit-, -mepoisson-, -menbreg- and -meologit-.
Note this course does not cover marginal or GEE type models, but will consider population-averaged effect measures derived from a generalised linear mixed model.
Software
Practical work will be done in Stata, and will be based on the Windows operating system. Note StataCorp will provide Statistical Services Centre Ltd with temporary licences for the current version of Stata. These short term licences will be made available to course participants prior to the course.
Cost:
£534
Website and registration:
https://www.ssc-training.co.uk/introduction-to-generalised-linear-mixed-models-using-stata.html
Region:
South East
Keywords:
Multilevel Modelling , Hierarchical models, Mixed models, Random effects, Quantitative data handling and data analysis, Generalised linear mixed models, mixed models, multilevel models, Hierarchical models, Clustered data, Logistic regression, Poisson regression, Ordinal regression, random effects
Related publications and presentations:
Multilevel Modelling
Hierarchical models
Mixed models
Random effects