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 [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$visit]])`). See details.
- weights
Character, either `"counterfactual"` (default), `"equal"`, `"proportional_em"` or `"proportional"`. Specifies the weighting strategy to be used when calculating the lsmeans. See the weighting section for more details.
Details
The function works as follows:
1. Select the first value from `visits`. 2. Subset the data to only the observations that occurred on this visit. 3. Fit a linear model as `vars$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 [set_vars()] e.g. `set_vars(covariates = c("sex*age"))`.