Linear Mixed Models for Repeated Measures using R
(online course)
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Overview
In a repeated measures experiment a response variable is repeatedly measured for each subject or unit over time under the same treatment. These observations are likely to be correlated over time, rendering conventional linear model methods either inappropriate for analysis or of limited use. Linear mixed models are commonly used to analyse repeated measurements, or longitudinal data, which are normally distributed. In this course we begin with a brief overview of repeated measures before moving onto the random coefficient model formulation of a linear mixed model (also known as subject-specific models). For the remainder of the course we focus on applying marginal models, sometimes known as covariance pattern models. Marginal models are particular useful for situations where the primary interest lies in studying mean trend through fixed effects, with variation in correlated errors about the trend treated as a nuisance. The course will emphasise the practicalities associated with choosing, fitting and interpreting linear mixed models in the context of analysing repeated measures. Examples will be drawn from medical and health related applications. Practical work will be based on the R software; see https://www.r-project.org/. Relevant models will be fitted using the CRAN packages lmerTest and mmrm. Presenters Sandro Leidi and James Gallagher Delivery Mode All training is online and will be delivered live each day between 10:00 and 16:30 (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 the analysis of repeated measures using linear mixed models. It is assumed that participants are R users and have some familiarity with the practical use of linear mixed models in general. No prior knowledge of analysing repeated measures is assumed. How You Will Benefit The course will give you the skills to use linear mixed models to analyse normally distributed repeated measurement data. You will also appreciate the distinction between random coefficient (subject-specific) models and marginal models, and their advantages and disadvantages. What Do We Cover?
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. Extra Information Related Courses: Introduction to Linear Mixed Models using R; Introduction to Linear Mixed Models in Health Research using Stata; Introduction to Generalised Linear Mixed Models using R; Introduction to Generalised Linear Mixed Models using Stata. |
Course Dates
23-24 September 2025 Venue: Online Duration: 2 days Price: £558 Apply Now (terms and conditions apply) Return to full course listing |
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