Skip to contents

Analysis of Covariance

Source: R/ancova_rbmi.R
rbmi_ancova.Rd

Performs an analysis of covariance between two groups returning the estimated "treatment effect" (i.e. the contrast between the two treatment groups) and the least square means estimates in each group.

Usage

rbmi_ancova(
  data,
  vars,
  visits = NULL,
  weights = c("counterfactual", "equal", "proportional_em", "proportional")
)

Arguments

data

A data.frame containing the data to be used in the model.

vars

A vars object as generated by rbmi::set_vars(). Only the group, visit, outcome and covariates elements are required. See details.

visits

An optional character vector specifying which visits to fit the ancova model at. If NULL, a separate ancova model will be fit to the outcomes for each visit (as determined by unique(data[[vars$visit]])). See details.

weights

Character, either "counterfactual" (default), "equal", "proportional_em" or "proportional". Specifies the weighting strategy to be used when calculating the lsmeans. See the weighting section for more details.

Value

a list of variance (var_*), treatment effect (trt_*), and least square mean (lsm_*) estimates for each visit, organized as described in Details above.

Details

The function works as follows:

  1. Select the first value from visits.

  2. Subset the data to only the observations that occurred on this visit.

  3. Fit a linear model as vars$outcome ~ vars$group + vars$covariates.

  4. Extract the "treatment effect" & least square means for each treatment group.

  5. Repeat points 2-3 for all other values in visits.

If no value for visits is provided then it will be set to unique(data[[vars$visit]]).

In order to meet the formatting standards set by rbmi_analyse() the results will be collapsed into a single list suffixed by the visit name, e.g.:

list(
   var_visit_1 = list(est = ...),
   trt_B_visit_1 = list(est = ...),
   lsm_A_visit_1 = list(est = ...),
   lsm_B_visit_1 = list(est = ...),
   var_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.

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

Note

These functions have the rbmi_ prefix to distinguish them from the corresponding rbmi package functions, from which they were copied from. Additional features here include:

  • Support for more than two treatment groups.

  • Variance estimates are returned.

See also

rbmi_analyse()

stats::lm()

rbmi::set_vars()