R package to fit Mixed Model for Repeated Measures as is commonly used
to analyze clinical trial data. This package uses nlme::gls
to fit the model,
and provides support for Kenward-Rogers degrees of freedom calculation.
This package is currently in beta version -- more testing and examples to come!
if(!requireNamespace("remotes")) install.packages("remotes")
remotes::install_github("alto-neuroscience/MMRM")
library(MMRM)
# fit an MMRM
my_mmrm = MMRM::mmrm(outcome ~ baseline + group + time + baseline:time + group:time,
time = "time",
subjects = "subjects",
data = my_data)
# fit MMRM using k-fold cross validation (steps below work the same)
my_mmrm = MMRM::mmrm_cv(outcome ~ baseline + group + time + baseline:time + group:time,
time = "time",
subjects = "subjects",
data = my_data,
k = 10)
# get estimated marginal means
mmrm_emm = MMRM::mmrm_emmeans(my_mmrm,
pairwise ~ group | time,
mode = "kenward")
# calculate effect size
mmrm_eff = MMRM::mmrm_eff_size(my_mmrm, mmrm_emm)