WP5 – Optimal design in mixed models to analyse studies in small population groups
Non-linear mixed effects models are used in model-based drug development to analyse all longitudinal data obtained during clinical trials. This is especially promising in small group trials. Therefore, finding good design for these studies is important to get precise results and/or good power especially when there are limitations on the sample size and on the number of sample/visit per patient.
Following our pioneer work in optimal design based on the Fisher Information Matrix for non-linear mixed effects models with continuous data, we wish to:
- Extend and evaluate this approach for longitudinal models with discrete data, repeated time to event and joint models
- Propose robust approaches with respect to parameter values as two-stage adaptive designs
- Propose robust approaches with respect to model uncertainty in design and analysis of pivotal trials analysed trough modelling
The developments will be made freely available in software tools, for instance as extensions of PFIM in R.
