In openpharma/mmrm: Mixed Models for Repeated Measures

mmrm

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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) for a tutorial and Mallinckrodt, Lane and Schnell (2008) 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: 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: can be obtained with the emmeans package
• One- and multi-dimensional linear contrasts of model parameters can be tested.
• Satterthwaite adjusted degrees of freedom.
• Kenward-Roger adjusted degrees of freedom and coefficients covariance matrix.
• Coefficient Covariance
• C++ backend:
• Fast implementation using C++ and automatic differentiation to obtain precise gradient information for model fitting.
• Model fitting algorithm details used in mmrm.
• Package ecosystems integration:
• Integration with tidymodels package ecosystem
  • Dedicated parsnip engine for linear regression
  • Integration with recipes
• Integration with tern package ecosystems
  • The 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:

install.packages("mmrm")

Development

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

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:

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 or get started by trying out the example:

Citing mmrm

To cite mmrm please see here.

openpharma/mmrm documentation built on April 14, 2025, 2:10 a.m.