The mixed model for repeated measures (MMRM) is a
popular model for longitudinal clinical trial data with
continuous endpoints, and brms a is powerful and versatile
package for fitting Bayesian regression models.
The brms.mmrm R package leverages brms to run MMRMs, and
it supports a simplified interfaced to reduce difficulty
and align with the best practices of the life sciences.
References
Bürkner, P.-C. (2017), "brms: An R package for Bayesian multilevel models using Stan," Journal of Statistical Software, 80, 1–28. https://doi.org/10.18637/jss.v080.i01.
Holzhauer, B., and Weber, S. (2024), "Bayesian mixed effects model for repeated measures," in Applied Modeling in Drug Development, Novartis AG. https://opensource.nibr.com/bamdd/src/02h_mmrm.html.
Mallinckrodt, C. H., Lane, P. W., Schnell, D., and others (2008), "Recommendations for the primary analysis of continuous endpoints in longitudinal clinical trials," Therapeutic Innovation and Regulatory Science, 42, 303–319. https://doi.org/10.1177/009286150804200402.
Mallinckrodt, C. H., and Lipkovich, I. (2017), Analyzing longitudinal clinical trial data: A practical guide, CRC Press, Taylor & Francis Group.