Untangling the Mess: Mixed Models
This session dives into how Mixed Models for Repeated Measures (MMRM) are reshaping the way complex trial data is analyzed. Participants will explore how tools like R’s lme4, nlme, and brms handle clustering, random effects, and repeated observations to extract meaningful insights from noisy datasets. Real-world examples, including mental health trials, illustrate how the right modeling approach reveals true treatment effects. Attendees will also learn when to use MMRM over traditional LOCF and how to validate assumptions for robust inference.
Session
Content
Updates
MMRM’s kicking LOCF’s butt for repeated measures. Cluster trials are blowing up.
Takeaway: Mixed models decode data.
Platforms
R’s lme4, nlme, brms handle fixed effects, random effects, clustering.
Takeaway: R makes messy data easy.
Strategy
Lean into MMRM, nail clustering for clear insights from chaotic designs.
Takeaway: Right model, right insights.
Latest Story
A mental health trial used lme4 to dig up treatment effects in clusters.
Takeaway: Mixed models turn noise to signal.
Analytics Challenge: Fit a mixed model in R with lme4
Key Takeaway
Mixed Models for Repeated Measures (MMRM) outperform traditional methods like LOCF by handling missing and correlated data more accurately. Tools like R’s lme4, nlme, and brms make it easier to model both fixed and random effects, revealing meaningful patterns in complex trial data. Applying clustering and hierarchical modeling helps uncover true treatment effects and transform noisy data into clear, reliable insights.