In the first day of the course the basic principles of multilevel analysis will be discussed with an example with a two-level structure and an example with a three-level structure.
In the second day of the course, some specific applications of multilevel analysis will be discussed, while in the third day of the course, the use of multilevel analysis for longitudinal data analysis will be discussed.
In day four and day five of the course, GEE-analysis, alternative modelling, growth curve models and the problem of missing data will be discussed. The last day of the course deals with the analysis of RCT data.
The course multilevel modelling and longitudinal data analysis is a six day course without specific modules or blocks. The schedule of the lectures is:
- Day 1: Basic principles of multilevel analysis, example of a two-level structure and example of a three-level structure.
- Day 2: The added value of multilevel analysis, sample size calculation, explanation of group differences, multilevel predictions, multivariate multilevel analysis and the analysis of other outcome variables.
- Day 3: Paired t-test, GLM for repeated measures, and longitudinal regression analysis.
- Day 4: Generalised estimating equations (GEE) and alternative models, such as time-lag and hybrid models.
- Day 5: Modelling time, analysing dichotomous outcomes, and handling missing data.
- Day 6: Analysis of data from randomised controlled trials (RCTs), including regression to the mean and longitudinal covariance analysis.
