Handling missing covariates in model-based meta-analysis (Use-case for competitive benchmarking)

Hosted by the MBMA Sub-SIG of the Statistics and Pharmacometrics SIG for ASA and ISoP
sxpsig.github.io/
/ isop-model-based-meta-...
Model-based meta-analysis (MBMA) is a quantitative approach that leverages summary- and/or individual-level of historical clinical trials to inform key drug development decisions. Missingness is a frequent limitation that we may face dealing with MBMA and which may lead to small cohort and biased conclusions. In literature, there exist several statistical approaches to handle missing information in MBMA.
In this webinar, we will focus particularly on missing values at the level of baseline characteristics rather than missing outcomes or other quantities. A use case of MBMA with fenebrutinib for competitive benchmarking in rheumatoid arthritis will be presented, and recommendations for best practices will be discussed.
Original presentation: Dec 7, 2023 11:00 AM Eastern Time (US and Canada)