Longitudinal Model-Based Meta-Analysis (MBMA): A Comprehensive MonolixSuite Tutorial
Model-based meta analysis (MBMA) informs key drug development decisions by integrating data, published or unpublished, from multiple studies. Due to these various sources of information and large number of individuals, MBMA models require careful implementation, and this webinar will show how to do it. Using Monolix, you will learn how to develop MBMA models from summary-level longitudinal data (e.g., mean responses over treatment arms), how to include between-study variability and between-treatment-arm variability, and how to apply appropriate weighting. The webinar will then show you how to use this model in Simulx for decision support by simulating a clinical trial while accounting for variability in parameter estimates. With the step-by-step presentation of two case studies, you will discover practical applications of @MonolixSuite to support model-informed drug development (MIDD).
Case study: https://monolixsuite.slp-software.com...
R script: https://monolixsuite.slp-software.com...
Q & A:
Q: How to deal with the situation that Narm vary with time?
A: As Narm is tagged as a regressor column, there is nothing specific to do: if it varies over time in the dataset, this will be taken into account.