Linear Mixed Models

Overview
Mixed modelling is a powerful tool for analysing data collected in experiments where the levels of a factor are a random sample from a wider selection, or where the data are from a multi-level structure with different levels of variability.  Practical situations are many and include medical applications, such as clinical trials, as well as industrial applications.
How to fit linear mixed models and interpret the results for a range of common situations is the subject of this course.

Who Should Attend?
Statisticians who are already familiar with General Linear Models.  It will be assumed that participants are SAS users.

How You Will Benefit
The course will give you the skills to formulate, fit and interpret mixed models for a range of practical situations, as well as an appreciation of some of the benefits of mixed modelling.

What Do We Cover?
  • Review of fixed and random effects
  • Concepts, models for simple situations, analysis of variance, expected mean squares and variance components
  • The theory of mixed models, REML, estimating fixed effects, degrees of freedom options
  • Model checking
  • Hierarchical structures
  • Multi-centre analyses
  • Mixed models for cross-over designs
  • Repeated measurements analysis
  • Practical experience, fitting models and interpreting the output.
Software
Practical work may be done in: SAS
Note: For practical work, participants must bring their own laptop with a fully licensed version of the software.

Extra Information
Related courses: Generalised Linear Mixed Models
Course Dates
27-29 April 2020

Venue: Reading, UK
Duration: 3 days
Price: £864

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