Components Density Plot — denscomp • viscomp

The function creates density plots in order to explore the efficacy of the components.

Usage

denscomp(
  model,
  sep = "+",
  combination,
  violin = FALSE,
  random = TRUE,
  z_value = FALSE
)

Arguments

model: An object of class netmeta.
sep: A single character that defines the separator between interventions components.
combination: A character vector that contains the component combinations of interest.
violin: logical. If TRUE the density is visualized via violins instead of density plots.
random: logical. If TRUE the random-effects NMA model is used, instead of the fixed-effect NMA model.
z_value: logical. If TRUE z-values are used, instead intervention effects.

Value

An object of class ggplot.

Details

If the length of the argument combination is 1, the function creates two density plots. The first is produced based on the interventions that include the component combination of interest (which is specified by the argument combination), while the second on the interventions that do not include the underlying component combination.

If the argument combination includes more than one elements, the number of densities is equal with the length of the argument combination, and each density is based on the interventions that include the relative component combination. For example, if combination = c("A + B", "B + C", "A") the function will produce 3 density plots that are based on the interventions that includes components "A" and "B", the interventions that include components "B" and "C" and interventions that includes component "A", respectively.

The function by default uses the intervention's relative effects (z_value = FALSE) obtained from the random-effects network meta-analysis (NMA) model (random = TRUE). It can be also adjusted to use the intervention's z-values instead of the relative effects, by setting z_value = TRUE.

Note

The efficacy of the components could be explored via violins plots instead of density plots, by setting violin = TRUE. Also, in the case of dichotomous outcomes, the log-scale is used.

The function can be applied only in network meta-analysis models that contain multi-component interventions.

Examples

data(nmaMACE)
denscomp(model = nmaMACE, combination = "C")