Package: brms.mmrm 1.1.0.9002

William Michael Landau

brms.mmrm: 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) <doi:10.18637/jss.v080.i01>, Mallinckrodt (2008) <doi:10.1177/009286150804200402>.

Authors:William Michael Landau [aut, cre], Kevin Kunzmann [aut], Yoni Sidi [aut], Christian Stock [aut], Eli Lilly and Company [cph, fnd], Boehringer Ingelheim Pharma GmbH & Co. KG [cph, fnd]

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NEWS

# Install 'brms.mmrm' in R:
install.packages('brms.mmrm', repos = c('https://openpharma.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/openpharma/brms.mmrm/issues

On CRAN:

brmslife-sciencesmc-stanmmrmstanstatistics

32 exports 18 stars 6.50 score 86 dependencies 316 mentions 17 scripts 1.1k downloads

Last updated 20 days agofrom:4a1643dd63. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 28 2024
R-4.5-winOKAug 28 2024
R-4.5-linuxOKAug 28 2024
R-4.4-winOKAug 28 2024
R-4.4-macOKAug 28 2024
R-4.3-winOKAug 28 2024
R-4.3-macOKAug 28 2024

Exports:brm_archetype_average_cellsbrm_archetype_average_effectsbrm_archetype_cellsbrm_archetype_effectsbrm_archetype_successive_cellsbrm_archetype_successive_effectsbrm_databrm_data_changebrm_data_chronologizebrm_formulabrm_formula_sigmabrm_marginal_databrm_marginal_drawsbrm_marginal_draws_averagebrm_marginal_gridbrm_marginal_probabilitiesbrm_marginal_summariesbrm_modelbrm_plot_comparebrm_plot_drawsbrm_prior_archetypebrm_prior_labelbrm_prior_simplebrm_prior_templatebrm_recenter_nuisancebrm_simulatebrm_simulate_categoricalbrm_simulate_continuousbrm_simulate_outlinebrm_simulate_priorbrm_simulate_simplebrm_transform_marginal

Dependencies:abindarrayhelpersbackportsbayesplotBHbinombridgesamplingbrmsBrobdingnagcallrcheckmateclicodacodetoolscolorspacecpp11descdigestdistributionaldplyrfansifarverfuturefuture.applygenericsggdistggplot2ggridgesglobalsgluegridExtragtablegtoolsinlineisobandlabelinglatticelifecyclelistenvloomagrittrMASSMatrixmatrixStatsmgcvmunsellmvtnormnleqslvnlmenumDerivparallellypillarpkgbuildpkgconfigplyrposteriorprocessxpspurrrquadprogQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelreshape2rlangrstanrstantoolsscalesStanHeadersstringistringrsvUnittensorAtibbletidybayestidyrtidyselecttrialrutf8vctrsviridisLitewithrzoo

BCVA data comparison between Bayesian and frequentist MMRMs

Rendered frombcva.Rmdusingknitr::rmarkdownon Aug 28 2024.

Last update: 2024-07-11
Started: 2024-02-12

FEV1 data comparison between Bayesian and frequentist MMRMs

Rendered fromfev1.Rmdusingknitr::rmarkdownon Aug 28 2024.

Last update: 2024-07-11
Started: 2024-02-09

Inference

Rendered frominference.Rmdusingknitr::rmarkdownon Aug 28 2024.

Last update: 2024-07-22
Started: 2024-04-04

Informative prior archetypes

Rendered fromarchetypes.Rmdusingknitr::rmarkdownon Aug 28 2024.

Last update: 2024-07-29
Started: 2024-05-29

Model

Rendered frommodel.Rmdusingknitr::rmarkdownon Aug 28 2024.

Last update: 2024-07-15
Started: 2024-04-04

Simulation

Rendered fromsimulation.Rmdusingknitr::rmarkdownon Aug 28 2024.

Last update: 2024-08-28
Started: 2023-09-12

Simulation-based calibration checking

Rendered fromsbc.Rmdusingknitr::rmarkdownon Aug 28 2024.

Last update: 2024-06-01
Started: 2023-12-14

Subgroup analysis

Rendered fromsubgroup.Rmdusingknitr::rmarkdownon Aug 28 2024.

Last update: 2024-07-11
Started: 2024-01-29

Usage

Rendered fromusage.Rmdusingknitr::rmarkdownon Aug 28 2024.

Last update: 2024-08-28
Started: 2023-06-06

Readme and manuals

Help Manual

Help pageTopics
brms.mmrm: Bayesian MMRMs using 'brms'brms.mmrm-package
Cell-means-like time-averaged archetypebrm_archetype_average_cells
Treatment effect time-averaged archetypebrm_archetype_average_effects
Cell means archetypebrm_archetype_cells
Treatment effect archetypebrm_archetype_effects
Cell-means-like successive differences archetypebrm_archetype_successive_cells
Treatment-effect-like successive differences archetypebrm_archetype_successive_effects
Create and preprocess an MMRM dataset.brm_data
Convert to change from baseline.brm_data_change
Chronologize a datasetbrm_data_chronologize
Model formulabrm_formula brm_formula.brms_mmrm_archetype brm_formula.default
Formula for standard deviation parametersbrm_formula_sigma
Marginal summaries of the data.brm_marginal_data
MCMC draws from the marginal posterior of an MMRMbrm_marginal_draws
Average marginal MCMC draws across time points.brm_marginal_draws_average
Marginal names grid.brm_marginal_grid
Marginal probabilities on the treatment effect for an MMRM.brm_marginal_probabilities
Summary statistics of the marginal posterior of an MMRM.brm_marginal_summaries
Fit an MMRM.brm_model
Visually compare the marginals of multiple models and/or datasets.brm_plot_compare
Visualize posterior draws of marginals.brm_plot_draws
Informative priors for fixed effects in archetypesbrm_prior_archetype
Label a prior with levels in the data.brm_prior_label
Simple prior for a 'brms' MMRMbrm_prior_simple
Label template for informative prior archetypesbrm_prior_template
Recenter nuisance variablesbrm_recenter_nuisance
Append simulated categorical covariatesbrm_simulate_categorical
Append simulated continuous covariatesbrm_simulate_continuous
Start a simulated datasetbrm_simulate_outline
Prior predictive draws.brm_simulate_prior
Simple MMRM simulation.brm_simulate_simple
Marginal mean transformationbrm_transform_marginal