brms.mmrm

R package to run Bayesian MMRMs using {brms}

https://github.com/openpharma/brms.mmrm

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R package to run Bayesian MMRMs using {brms}

README.Rmd

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# brms.mmrm 

[![CRAN](https://www.r-pkg.org/badges/version/brms.mmrm)](https://CRAN.R-project.org/package=brms.mmrm)
[![status](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
[![check](https://github.com/openpharma/brms.mmrm/workflows/check/badge.svg)](https://github.com/openpharma/brms.mmrm/actions?query=workflow%3Acheck)
[![cover](https://github.com/openpharma/brms.mmrm/workflows/cover/badge.svg)](https://github.com/openpharma/brms.mmrm/actions?query=workflow%3Acover)
[![lint](https://github.com/openpharma/brms.mmrm/workflows/lint/badge.svg)](https://github.com/openpharma/brms.mmrm/actions?query=workflow%3Alint)

The [mixed model for repeated measures (MMRM)](https://link.springer.com/article/10.1177/009286150804200402) is a popular model for longitudinal clinical trial data with continuous endpoints, and [`brms`](https://paulbuerkner.com/brms/) is powerful and versatile package for fitting Bayesian regression models. The `brms.mmrm` R package leverages [`brms`](https://paulbuerkner.com/brms/) to run [MMRMs](https://link.springer.com/article/10.1177/009286150804200402), and it supports a simplified interface to reduce difficulty and align with best practices for the life sciences.

## Installation

Type | Source | Command
---|---|---
Release | CRAN | `install.packages("brms.mmrm")`
Development | GitHub | `remotes::install_github("openpharma/brms.mmrm")`
Development | openpharma | `install.packages("brms.mmrm", repos = "https://openpharma.r-universe.dev")`

## Documentation

The documentation website at  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`](https://openpharma.github.io/mmrm/latest-tag/) package on two example datasets. The analyses and results are described in the package vignettes linked below:

* [Simulation-based calibration](https://openpharma.github.io/brms.mmrm/articles/sbc.html)
* [FEV1 data comparison between Bayesian and frequentist MMRMs](https://openpharma.github.io/brms.mmrm/articles/fev1.html).
* [BCVA data comparison between Bayesian and frequentist MMRMs](https://openpharma.github.io/brms.mmrm/articles/bcva.html).

Notably, [FEV1](https://openpharma.github.io/mmrm/latest-tag/reference/fev_data.html) and [BCVA](https://openpharma.github.io/mmrm/latest-tag/reference/bcva_data.html) are the same datasets that [`mmrm`](https://openpharma.github.io/mmrm/latest-tag/) uses to compare itself against SAS in [this vignette](https://openpharma.github.io/mmrm/latest-tag/articles/mmrm_review_methods.html). 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`](https://openpharma.github.io/mmrm/latest-tag/) and/or SAS.

## Help

Please report questions and problems as [GitHub discussions](https://github.com/openpharma/brms.mmrm) and [GitHub issues](https://github.com/openpharma/brms.mmrm), respectively.

## Thanks

Thanks to the [`openstatsware`](https://www.openstatsware.org/) and [R Consortium](https://r-consortium.org/) 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](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

## Citation

```{r, warning = FALSE, comment = NA_character_, echo = FALSE}
x <- packageDescription("brms.mmrm")
x$URL <- "https://github.com/openpharma/brms.mmrm"
citation(package = "brms.mmrm", auto = x)
```

## 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. .

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