Bayesian MMRMs using 'brms'
Cell-means-like time-averaged archetype
Treatment effect time-averaged archetype
Cell means archetype
Treatment effect archetype
Cell-means-like successive differences archetype
Treatment-effect-like successive differences archetype
Convert to change from baseline.
Chronologize a dataset
Create and preprocess an MMRM dataset.
Formula for standard deviation parameters
Model formula
Marginal summaries of the data.
Average marginal MCMC draws across time points.
MCMC draws from the marginal posterior of an MMRM
Marginal names grid.
Marginal probabilities on the treatment effect for an MMRM.
Summary statistics of the marginal posterior of an MMRM.
Fit an MMRM.
Visually compare the marginals of multiple models and/or datasets.
Visualize posterior draws of marginals.
Informative priors for fixed effects in archetypes
Label a prior with levels in the data.
Simple prior for a brms MMRM
Label template for informative prior archetypes
Recenter nuisance variables
Append simulated categorical covariates
Append simulated continuous covariates
Start a simulated dataset
Prior predictive draws.
Simple MMRM simulation.
Deprecated: simulate an MMRM.
Marginal mean transformation
brms.mmrm: Bayesian MMRMs using brms
Summarize an informative prior archetype.
Summarize marginal transform.
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>.
Useful links