Bayesian MMRMs using 'brms'

The mixed model for repeated measures (MMRM) is a popular model for longitudinal clinical trial data with continuous endpoints, and 'brms' is a 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 (2017) , Mallinckrodt (2008) .

brms.mmrm

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 interface to reduce difficulty and align with best practices for the life sciences.

Installation

Documentation

The documentation website at https://openpharma.github.io/brms.mmrm/ has a complete function reference and tutorial vignettes.

Validation

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:

• Simulation-based calibration
• FEV1 data comparison between Bayesian and frequentist MMRMs.
• BCVA data comparison between Bayesian and frequentist MMRMs.

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 similar analyses on your own datasets to compare brms.mmrm against mmrm and/or SAS.

Help

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

Thanks

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.

Citation

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 1.1.0.9002,
  <https://github.com/openpharma/brms.mmrm>.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {brms.mmrm: Bayesian MMRMs using 'brms'},
    author = {William Michael Landau and Kevin Kunzmann and Yoni Sidi and Christian Stock},
    note = {R package version 1.1.0.9002},
    url = {https://github.com/openpharma/brms.mmrm},
  }

References

• 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).
• 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.