ancova_single: Implements an Analysis of Covariance (ANCOVA)

In rbmi: Reference Based Multiple Imputation

Implements an Analysis of Covariance (ANCOVA)

Description

Performance analysis of covariance. See ancova() for full details.

Usage

ancova_single(
  data,
  outcome,
  group,
  covariates,
  weights = c("counterfactual", "equal", "proportional_em", "proportional")
)

Arguments

data	A data.frame containing the data to be used in the model.
outcome	Character, the name of the outcome variable in data.
group	Character, the name of the group variable in data.
covariates	Character vector containing the name of any additional covariates to be included in the model as well as any interaction terms.
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

• group must be a factor variable with only 2 levels.

• outcome must be a continuous numeric variable.

Weighting

Counterfactual

For weights = "counterfactual" (the default) the lsmeans are obtained by taking the average of the predicted values for each patient after assigning all patients to each arm in turn. This approach is equivalent to standardization or g-computation. In comparison to emmeans this approach is equivalent to:

emmeans::emmeans(model, specs = "<treatment>", counterfactual = "<treatment>")

Note that to ensure backwards compatibility with previous versions of rbmi weights = "proportional" is an alias for weights = "counterfactual". To get results consistent with emmeans's weights = "proportional" please use weights = "proportional_em".

Equal

For weights = "equal" the lsmeans are obtained by taking the model fitted value of a hypothetical patient whose covariates are defined as follows:

• Continuous covariates are set to mean(X)

• Dummy categorical variables are set to 1/N where N is the number of levels

• Continuous * continuous interactions are set to mean(X) * mean(Y)

• Continuous * categorical interactions are set to mean(X) * 1/N

• Dummy categorical * categorical interactions are set to 1/N * 1/M

In comparison to emmeans this approach is equivalent to:

emmeans::emmeans(model, specs = "<treatment>", weights = "equal")

Proportional

For weights = "proportional_em" the lsmeans are obtained as per weights = "equal" except instead of weighting each observation equally they are weighted by the proportion in which the given combination of categorical values occurred in the data. In comparison to emmeans this approach is equivalent to:

emmeans::emmeans(model, specs = "<treatment>", weights = "proportional")

Note that this is not to be confused with weights = "proportional" which is an alias for weights = "counterfactual".

See Also

ancova()

Examples

## Not run: 
iris2 <- iris[ iris$Species %in% c("versicolor", "virginica"), ]
iris2$Species <- factor(iris2$Species)
ancova_single(iris2, "Sepal.Length", "Species", c("Petal.Length * Petal.Width"))

## End(Not run)

rbmi documentation built on Oct. 16, 2024, 5:09 p.m.