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https://github.com/openpharma/mmrm

Mixed Models for Repeated Measures (MMRM) in R.
https://github.com/openpharma/mmrm

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Mixed Models for Repeated Measures (MMRM) in R.

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README 

        
---

output: github_document

---



# mmrm  



```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```



[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)

[![CRAN status](https://www.r-pkg.org/badges/version-last-release/mmrm)](https://www.r-pkg.org/badges/version-last-release/mmrm)

[![CRAN monthly downloads](https://cranlogs.r-pkg.org/badges/mmrm)](https://cranlogs.r-pkg.org/badges/mmrm)

[![CRAN total downloads](https://cranlogs.r-pkg.org/badges/grand-total/mmrm)](https://cranlogs.r-pkg.org/badges/grand-total/mmrm)

[![Code Coverage](https://raw.githubusercontent.com/openpharma/mmrm/_xml_coverage_reports/data/main/badge.svg)](https://openpharma.github.io/mmrm/latest-tag/coverage-report/)



\



Mixed models for repeated measures (MMRM) are a popular

choice for analyzing longitudinal continuous outcomes in randomized

clinical trials and beyond; see

[Cnaan, Laird and Slasor (1997)](https://doi.org/10.1002%2f%28SICI%291097-0258%2819971030%2916%3a20%3c2349%3a%3aAID-SIM667%3e3.0.CO%3b2-E)

for a tutorial and

[Mallinckrodt, Lane and Schnell (2008)](https://doi.org/10.1177/009286150804200402)

for a review. This package implements

MMRM based on the marginal linear model without random effects using

Template Model Builder (`TMB`) which enables fast and robust model

fitting. Users can specify a variety of covariance matrices, weight

observations, fit models with restricted or standard maximum

likelihood inference, perform hypothesis testing with Satterthwaite

or Kenward-Roger adjustment, and extract least

square means estimates by using `emmeans`.



**Scope:**



* Continuous responses with normal (but potentially heteroscedastic) residuals.

* Marginal linear models (no individual-level random effects).



**Main Features:**



* Flexible covariance specification:

  * [Structures](https://openpharma.github.io/mmrm/main/articles/covariance.html): unstructured, Toeplitz, AR1, compound symmetry, ante-dependence, and spatial exponential.

  * Groups: shared covariance structure for all subjects or group-specific

covariance estimates.

  * Variances: homogeneous or heterogeneous across time points.

* Inference:

  * Supports REML and ML.

  * Supports weights.

* Hypothesis testing:

  * [Least square means](https://openpharma.github.io/mmrm/main/reference/emmeans_support.html):

    can be obtained with the `emmeans` package

  * One- and multi-dimensional linear contrasts of model parameters can be tested.

  * [Satterthwaite](https://openpharma.github.io/mmrm/main/articles/satterthwaite.html)

    adjusted degrees of freedom.

  * [Kenward-Roger](https://openpharma.github.io/mmrm/main/articles/kenward.html)

    adjusted degrees of freedom and coefficients covariance matrix.

  * [Coefficient Covariance](https://openpharma.github.io/mmrm/main/articles/coef_vcov.html)

* `C++` backend:

  * Fast implementation using `C++` and automatic differentiation to

    obtain precise gradient information for model fitting.

  * Model fitting algorithm [details](https://openpharma.github.io/mmrm/main/articles/algorithm.html) used in `mmrm`.

* Package ecosystems integration:

  * Integration with [tidymodels](https://www.tidymodels.org/) package ecosystem

    * Dedicated [parsnip](https://parsnip.tidymodels.org/) engine for linear regression

    * Integration with [recipes](https://recipes.tidymodels.org/)

  * Integration with [tern](https://insightsengineering.github.io/tern/) package ecosystems

    * The [tern.mmrm](https://insightsengineering.github.io/tern.mmrm/) package can be used to run the `mmrm` fit and generate tabulation and plots of least square means per visit and treatment arm, tabulation of model diagnostics, diagnostic graphs, and other standard model outputs.



## Installation



### Release



You can install the current release version from *CRAN* with:



```{r cran-installation, eval = FALSE}

install.packages("mmrm")

```



### Development



You can install the current development version from *R-Universe* with:



```{r ru-installation, eval = FALSE}

install.packages(

  "mmrm",

  repos = c("https://openpharma.r-universe.dev", "https://cloud.r-project.org")

)

```



This is preferred, because for Windows and MacOS systems you can install

pre-compiled binary versions of the packages, avoiding the need for compilation.



Alternatively, you can install the current development version from

*GitHub* with:



```{r gh-installation, eval = FALSE}

if (!require("remotes")) {

  install.packages("remotes")

}

remotes::install_github("openpharma/mmrm")

```



Note that this installation from source can take a substantial amount of time,

because compilation of the `C++` sources is required.



## Getting Started



See also the [introductory vignette](https://openpharma.github.io/mmrm/main/articles/introduction.html)

or get started by trying out the example:



```{r, child='vignettes/subsections/_intro-getting_started.Rmd'}

```



## Citing `mmrm`



To cite `mmrm` please see [here](https://openpharma.github.io/mmrm/main/authors.html#citation).