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mmrm: Mixed Models for Repeated Measures

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) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) <doi: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'.

Version: 0.3.14
Depends: R (≥ 4.0)
Imports: checkmate (≥ 2.0), generics, lifecycle, Matrix, methods, nlme, parallel, Rcpp, Rdpack, stats, stringr, tibble, TMB (≥ 1.9.1), utils
LinkingTo: Rcpp, RcppEigen, testthat, TMB (≥ 1.9.1)
Suggests: broom.helpers, car (≥ 3.1.2), cli, clubSandwich, clusterGeneration, dplyr, emmeans (≥ 1.6), estimability, ggplot2, glmmTMB, hardhat, knitr, lme4, MASS, microbenchmark, mockery, parallelly (≥ 1.32.0), parsnip (≥ 1.1.0), purrr, rmarkdown, sasr, scales, testthat (≥ 3.0.0), tidymodels, withr, xml2
Published: 2024-09-27
DOI: 10.32614/CRAN.package.mmrm
Author: Daniel Sabanes Bove ORCID iD [aut, cre], Liming Li [aut], Julia Dedic [aut], Doug Kelkhoff [aut], Kevin Kunzmann [aut], Brian Matthew Lang [aut], Christian Stock [aut], Ya Wang [aut], Craig Gower-Page [ctb], Dan James [aut], Jonathan Sidi [aut], Daniel Leibovitz [aut], Daniel D. Sjoberg ORCID iD [aut], Lukas A. Widmer ORCID iD [ctb], Boehringer Ingelheim Ltd. [cph, fnd], Gilead Sciences, Inc. [cph, fnd], F. Hoffmann-La Roche AG [cph, fnd], Merck Sharp & Dohme, Inc. [cph, fnd], AstraZeneca plc [cph, fnd], inferential.biostatistics GmbH [cph, fnd]
Maintainer: Daniel Sabanes Bove <daniel.sabanes_bove at rconis.com>
BugReports: https://github.com/openpharma/mmrm/issues
License: Apache License 2.0
URL: https://openpharma.github.io/mmrm/
NeedsCompilation: yes
Language: en-US
Materials: NEWS
In views: ClinicalTrials, MixedModels
CRAN checks: mmrm results

Documentation:

Reference manual: mmrm.pdf
Vignettes: Model Fitting Algorithm (source, R code)
Between-Within (source, R code)
Coefficients Covariance Matrix Adjustment (source, R code)
Covariance Structures (source, R code)
Details of Weighted Least Square Empirical Covariance (source)
Details of Hypothesis Testing (source, R code)
Package Introduction (source, R code)
Kenward-Roger (source, R code)
Mixed Models for Repeated Measures (source, R code)
Comparison with other software (source, R code)
Package Structure (source)
Prediction and Simulation (source, R code)
Satterthwaite (source, R code)

Downloads:

Package source: mmrm_0.3.14.tar.gz
Windows binaries: r-devel: mmrm_0.3.14.zip, r-release: mmrm_0.3.14.zip, r-oldrel: mmrm_0.3.14.zip
macOS binaries: r-release (arm64): mmrm_0.3.14.tgz, r-oldrel (arm64): mmrm_0.3.14.tgz, r-release (x86_64): mmrm_0.3.14.tgz, r-oldrel (x86_64): mmrm_0.3.14.tgz
Old sources: mmrm archive

Reverse dependencies:

Reverse imports: rbmi, tern.mmrm
Reverse suggests: brms.mmrm, broom.helpers, insight, parameters

Linking:

Please use the canonical form https://CRAN.R-project.org/package=mmrm to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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