Components Rank Heat Plot — rankheatplot • viscomp

Rank heat plot summarizes the components' p-scores for multiple outcomes.

Usage

rankheatplot(
  model,
  sep = "+",
  median = TRUE,
  random = TRUE,
  outcomeNames = NULL,
  cex_components = NULL,
  cex_values = NULL,
  cex_outcomes = NULL
)

Arguments

model: A list of netmeta models.
sep: A single character that defines the separator between interventions components.
median: logical. If TRUE the median is used as a summary measure instead of the mean.
random: A logical vector that specifies the NMA model for each outcome. If TRUE the random-effects NMA model is used instead of the fixed-effects NMA model.
outcomeNames: A character vector that specifies the names of the outcomes.
cex_components: Font size of components' names.
cex_values: Font size of p-scores.
cex_outcomes: Font size of outcomes' names.

Value

Returns (invisibly) a rank heat plot.

Details

The function creates a rank heat plot, where the number of circles depend on the number of outcomes. Each circle is divided by the total number of components, and each sector is colored according the corresponding component p-score. Components' p-scores are summarized by using either the median (median = TRUE) or the mean (median = FALSE) of the p-scores obtained from the interventions that include the corresponding component. The sector's colors reflect the magnitude of the components p-scores. Red color indicates a low p-score (close to zero), while green color indicates values close to 1. Intervention's p-scores are obtained from the network meta-analysis (NMA) model. By default the random-effects NMA model is used for each outcome (random = TRUE).

Note

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

Examples

# \donttest{
# Artificial data set

t1 <- c("A", "B", "C", "A+B", "A+C", "B+C", "A")
t2 <- c("C", "A", "A+C", "B+C", "A", "B", "B+C")

TE1 <- c(2.12, 3.24, 5.65, -0.60, 0.13, 0.66, 3.28)
TE2 <- c(4.69, 2.67, 2.73, -3.41, 1.79, 2.93, 2.51)

seTE1 <- rep(0.1, 7)
seTE2 <- rep(0.2, 7)

study <- paste0("study_", 1:7)

data1 <- data.frame(
  "TE" = TE1, "seTE" = seTE1, "treat1" = t1, "treat2" = t2, "studlab" = study,
  stringsAsFactors = FALSE
)

data2 <- data.frame(
  "TE" = TE2, "seTE" = seTE2, "treat1" = t1, "treat2" = t2, "studlab" = study,
  stringsAsFactors = FALSE
)

# Network meta-analysis models

net1 <- netmeta::netmeta(
  TE = TE, seTE = seTE, studlab = studlab, treat1 = treat1,
  treat2 = treat2, data = data1, ref = "A"
)

net2 <- netmeta::netmeta(
  TE = TE, seTE = seTE, studlab = studlab, treat1 = treat1,
  treat2 = treat2, data = data2, ref = "A"
)

# Rank heat plot

rankheatplot(model = list(net1, net2))

# }