Skip to contents

MMRM Analysis for Imputed Datasets

Source: R/mmrm_rbmi.R
rbmi_mmrm.Rd

Performs an MMRM for two or more groups returning the estimated 'treatment effect' (i.e. the contrast between treatment groups and the control group) and the least square means estimates in each group.

Usage

rbmi_mmrm(
  data,
  vars,
  cov_struct = c("us", "toep", "cs", "ar1"),
  visits = NULL,
  weights = c("counterfactual", "equal"),
  ...
)

Arguments

data

(`data.frame`)
containing the data to be used in the model.

vars

(`vars`)
list as generated by [set_vars()]. Only the `subjid`, `group`, `visit`, `outcome` and `covariates` elements are required. See details.

cov_struct

(`string`)
the covariance structure to use. Note that the same covariance structure is assumed for all treatment groups.

visits

(`NULL` or `character`)
An optional character vector specifying which visits to fit the MMRM at. If `NULL`, the MMRM model will be fit to the whole dataset.

weights

(`string`)
the weighting strategy to be used when calculating the least square means, either `'counterfactual'` or `'equal'`.

...

additional arguments passed to [mmrm::mmrm()], in particular `method` and `vcov` to control the degrees of freedom and variance-covariance adjustment methods as well as `reml` decide between REML and ML estimation.

Details

The function works as follows:

1. Optionally select the subset of the `data` corresponding to `visits. 2. Fit an MMRM as `vars$outcome ~ vars$group + vars$visit + vars$covariates` with the specified covariance structure for visits within subjects. 3. Extract the 'treatment effect' & least square means for each treatment group vs the control group.

In order to meet the formatting standards set by [analyse()] the results will be collapsed into a single list suffixed by the visit name, e.g.: “` list( var_B_visit_1 = list(est = ...), trt_B_visit_1 = list(est = ...), lsm_A_visit_1 = list(est = ...), lsm_B_visit_1 = list(est = ...), var_B_visit_2 = list(est = ...), trt_B_visit_2 = list(est = ...), lsm_A_visit_2 = list(est = ...), lsm_B_visit_2 = list(est = ...), ... ) “` Please note that 'trt' refers to the treatment effects, and 'lsm' refers to the least square mean results. In the above example `vars$group` has two factor levels A and B. The new 'var' refers to the model estimated variance of the residuals at the given visit, together with the degrees of freedom (which is treatment group specific).

If you want to include additional interaction terms in your model this can be done by providing them to the `covariates` argument of [set_vars()] e.g. `set_vars(covariates = c('sex*age'))`.

Note

The `group` and `visit` interaction `group:visit` is not included by default in the model, therefore please add that to `covariates` manually if you want to include it. This will make sense in most cases.

See also

[rbmi_analyse()]

[mmrm::mmrm()]

[set_vars()]