Description Usage Arguments Value Examples

This function creates an mmrm stat object which can be passed as input
to the `set_stats()`

function when building an aba model. This stat performs
a MMRM analysis using the `gls`

function from the
`nlme`

package. Please note that the default mode is to include an interaction
term between the `time`

variable and each predictor - i.e., `time*predictor`

will be in the model formula - but this does not happen for covariates.
The data for this model should be in long format with one row per
subject-visit.

1 2 3 4 5 6 7 8 |

`id` |
string. This is the variable in the data which represents the subject id to be used for random intercepts and random slopes. |

`time` |
string. This is the time variable in the data which represents
the time from baseline that the visit occured. This should be a categorical
variable or a continuous variable where the values are shared by
all subjects. The fact that time visits should be common across all subjects
is a major operational difference from |

`treatment` |
string. The treatment variable whose effect on the outcome
you care about. This is useful for |

`baseline_suffix` |
string. The suffix to add to each outcome variable in order to pick up the associated baseline variable. You must adjust for the baseline outcome in mmrm, and there is no other way to specify a different predictor for each outcome. So if the outcomes are e.g. "CDRSB" and "MMSE", then a baseline_suffix of "bl" will mean that each mmrm fit with "CDRSB" as outcome will have "CDRSB_bl" added to the formula and every fit with "MMSE" as outcome will have "MMSE_bl" added. This means that these baseline variables must actually exist in the data. Also, there will always be an interaction between the baseline outcome variable and the time variable. |

`std.beta` |
logical. Whether to standardize model predictors and covariates prior to analysis. |

`complete.cases` |
logical. Whether to only include the subset of data with no missing data for any of the outcomes, predictors, or covariates. Note that complete cases are considering within each group - outcome combination but across all predictor sets. |

An abaStat object with `mmrm`

stat type.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
data <- adnimerge %>%
dplyr::filter(VISCODE %in% c('bl','m06','m12','m24'))
model <- data %>% aba_model() %>%
set_groups(
DX_bl %in% c('MCI', 'AD')
) %>%
set_outcomes(CDRSB, ADAS13) %>%
set_predictors(
PLASMA_ABETA_bl,
PLASMA_PTAU181_bl,
PLASMA_NFL_bl
) %>%
set_covariates(AGE, GENDER, EDUCATION) %>%
set_stats(
stat_mmrm(id = 'RID', time = 'VISCODE')
) %>%
fit()
model_summary <- model %>% aba_summary()
``` |

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