ancova {rbmi}

Analysis of Covariance

Description

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

ancova(data, vars, visits = NULL, weights = c("proportional", "equal"))

Arguments

data

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

vars

A vars object as generated by 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 "proportional" (default) or "equal". Specifies the weighting strategy to be used for categorical covariates when calculating the lsmeans. See details.

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 analyse() the results will be collapsed into a single list suffixed by the visit name, e.g.:

list(
   trt_visit_1 = list(est = ...),
   lsm_ref_visit_1 = list(est = ...),
   lsm_alt_visit_1 = list(est = ...),
   trt_visit_2 = list(est = ...),
   lsm_ref_visit_2 = list(est = ...),
   lsm_alt_visit_2 = list(est = ...),
   ...
)

Please note that "ref" refers to the first factor level of vars$group which does not necessarily coincide with the control arm. Analogously, "alt" refers to the second factor level of vars$group. "trt" refers to the model contrast translating the mean difference between the second level and first level.

If you want to include 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")).

Weighting

"proportional" is the default scheme that is used. This is equivalent to standardization, i.e. the lsmeans in each group are equal to the predicted mean outcome from the ancova model for that group based on baseline characteristics of all subjects regardless of their assigned group. The alternative weighting scheme, "equal", creates hypothetical patients by expanding out all combinations of the models categorical covariates. The lsmeans are then calculated as the average of the predicted mean outcome for these hypothetical patients assuming they come from each group in turn.

In short:

• "proportional" weights categorical covariates based upon their frequency of occurrence in the data.

• "equal" weights categorical covariates equally across all theoretical combinations.

See Also

analyse()

stats::lm()

set_vars()