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`ANCOVA` Analysis

Source: R/ancova.R
fit_ancova.Rd

Does the `ANCOVA` analysis, separately for each visit.

Usage

fit_ancova(
  vars = list(response = "AVAL", covariates = c(), arm = "ARM", visit = "AVISIT", id =
    "USUBJID"),
  data,
  conf_level = 0.95,
  weights_emmeans = "proportional"
)

Arguments

vars

(named `list` of `string` or `character`)
specifying the variables in the `ANCOVA` analysis. The following elements need to be included as character vectors and match corresponding columns in `data`:

- `response`: the response variable. - `covariates`: the additional covariate terms (might also include interactions). - `id`: the subject ID variable (not really needed for the computations but for internal logistics). - `arm`: the treatment group variable (factor). - `visit`: the visit variable (factor).

Note that the `arm` variable is by default included in the model, thus should not be part of `covariates`.

data

(`data.frame`)
with all the variables specified in `vars`. Records with missing values in any independent variables will be excluded.

conf_level

(`proportion`)
confidence level of the interval.

weights_emmeans

(`string`)
argument from [emmeans::emmeans()], `'counterfactual'` by default.

Value

A `tern_model` object which is a list with model results:

- `fit`: A list with a fitted [stats::lm()] result for each visit. - `mse`: Mean squared error, i.e. variance estimate, for each visit. - `df`: Degrees of freedom for the variance estimate for each visit. - `lsmeans`: This is a list with data frames `estimates` and `contrasts`. The attribute `weights` savse the settings used (`weights_emmeans`). - `vars`: The variable list. - `labels`: Corresponding list with variable labels extracted from `data`. - `ref_level`: The reference level for the arm variable, which is always the first level. - `treatment_levels`: The treatment levels for the arm variable. - `conf_level`: The confidence level which was used to construct the `lsmeans` confidence intervals.

Examples

library(mmrm)
#> mmrm() registered as emmeans extension

fit <- fit_ancova(
  vars = list(
    response = 'FEV1',
    covariates = c('RACE', 'SEX'),
    arm = 'ARMCD',
    id = 'USUBJID',
    visit = 'AVISIT'
  ),
  data = fev_data,
  conf_level = 0.9,
  weights_emmeans = 'equal'
)