Repeated Measurements Analysis

Overview
In a repeated measurements experiment a sequence of observations is collected for each subject or unit. This raises certain complexities in terms of the analysis of the data - especially since the observations are likely to be correlated over time, rendering conventional statistical methods inappropriate.
During the course the main approaches to dealing with repeated normal data are covered, from simple methods to more complex modelling, especially mixed modelling. The practicalities associated with choosing, fitting and interpreting models will also be addressed.

Who Should Attend?
Statisticians who need to analyse data from repeated measurements experiments. No previous experience of repeated measurements is required, but knowledge of linear modelling and analysis of variance is assumed. Knowledge of SAS is also assumed.

How You Will Benefit
The course will give you the skills to apply a range of repeated measurements analysis methods for normal data and an appreciation of their relative advantages and disadvantages.

What Do We Cover?
  • Overview of methods of analysis
  • Summary statistics approach
  • Split-plot-in-time ANOVA
  • Mixed models for repeated measurements; likelihood methods (ML and REML)
  • Fixed effects, or marginal, models; different covariance structures and selection of covariance structures
  • Random regression coefficient models
  • Random regression coefficient versus marginal models
  • Model checking
  • Further complexities associated with repeated measurements analysis.
Software
Practical work will 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: Modelling Binary and Categorical Repeated Measurements Data; Linear Mixed Models
Course Dates
12 - 13 October 2020

Venue: Reading, UK
Duration: 2 days
Price: £588

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