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The mixed model for repeated measures (MMRM) is a popular model for longitudinal clinical trial data with continuous endpoints, and brms 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 best practices for the life sciences.


Type Source Command
Release CRAN install.packages("brms.mmrm")
Development GitHub remotes::install_github("openpharma/brms.mmrm")
Development openpharma install.packages("brms.mmrm", repos = "")


The documentation website at has a complete function reference and tutorial vignettes.


To ensure the correctness of the model and its implementation, this package has been validated using simulation-based calibration and comparisons against the frequentist mmrm package on two example datasets. The analyses and results are described in the package vignettes linked below:

Notably, FEV1 and BCVA are the same datasets that mmrm uses to compare itself against SAS in this vignette. For additional validation in your functional area or domain of expertise, you may choose to run the similar analyses on your own datasets to compare brms.mmrm against mmrm and/or SAS.


Please report questions and problems as GitHub discussions and GitHub issues, respectively.


Thanks to the openstatsware and R Consortium for providing professional networks to recruit skilled statisticians and developers.

Code of conduct

Please note that the brms.mmrm project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.


To cite package 'brms.mmrm' in publications use:

  Landau WM, Kunzmann K, Sidi Y, Stock C (????). _brms.mmrm: Bayesian
  MMRMs using 'brms'_. R package version,,

A BibTeX entry for LaTeX users is

    title = {brms.mmrm: Bayesian MMRMs using 'brms'},
    author = {William Michael Landau and Kevin Kunzmann and Yoni Sidi and Christian Stock},
    note = {R package version,},
    url = {},


  • Paul-Christian Bürkner (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1-28.
  • Mallinckrodt, C.H., Lane, P.W., Schnell, D. et al. Recommendations for the Primary Analysis of Continuous Endpoints in Longitudinal Clinical Trials. Ther Innov Regul Sci 42, 303–319 (2008).