netmeta (version 1.2-1)

plot.netrank: Plot treatment ranking(s) of network meta-analyses

Description

Produce an image plot of treatment ranking(s) generated with R function netrank.

Usage

# S3 method for netrank
plot(
  ...,
  name,
  comb.fixed,
  comb.random,
  seq,
  low = "red",
  mid = "yellow",
  high = "green",
  col = "black",
  main,
  main.size = 14,
  main.col = col,
  main.face = "bold",
  legend = TRUE,
  axis.size = 12,
  axis.col = col,
  axis.face = "plain",
  na.value = "grey50",
  angle = 45,
  hjust.x = 1,
  vjust.x = 1,
  hjust.y = 1,
  vjust.y = 0,
  nchar.trts,
  digits = 3
)

Arguments

...

A single netrank object or a list of netrank objects.

name

An optional character vector providing descriptive names for the network meta-analysis objects.

comb.fixed

A logical indicating whether results for the fixed effects (common effects) model should be plotted.

comb.random

A logical indicating whether results for the random effects model should be plotted.

seq

A character or numerical vector specifying the sequence of treatments on the x-axis.

low

A character string defining the colour for a P-score of 0, see scale_fill_gradient2.

mid

A character string defining the colour for a P-score of 0.5, see scale_fill_gradient2.

high

A character string defining the colour for a P-score of 1, see scale_fill_gradient2.

col

Colour of text.

main

Title.

main.size

Font size of title, see element_text.

main.col

Colour of title, see element_text.

main.face

Font face of title, see element_text.

legend

A logical indicating whether a legend should be printed.

axis.size

Font size of axis text, see element_text.

axis.col

Colour of axis text, see element_text.

axis.face

Font face of axis text, see element_text.

na.value

Colour for missing values, see scale_fill_gradient2.

angle

Angle for text on x-axis, see element_text.

hjust.x

A numeric between 0 and 1 with horizontal justification of text on x-axis, see element_text.

vjust.x

A numeric between 0 and 1 with vertical justification of text on x-axis, see element_text.

hjust.y

A numeric between 0 and 1 with horizontal justification of text on y-axis, see element_text.

vjust.y

A numeric between 0 and 1 with vertical justification of text on y-axis, see element_text.

nchar.trts

A numeric defining the minimum number of characters used to create unique treatment names.

digits

Minimal number of significant digits, see print.default.

Value

A ggplot2 object.

Details

This function produces an image plot of network rankings (Palpacuer et al., 2018, Figure 4). Note, a scatter plot of two network rankings can be generated with plot.netposet.

By default, treatments are ordered by decreasing P-scores of the first network meta-analysis object. Argument seq can be used to specify a differenct treatment order.

References

Palpacuer C, Duprez R, Huneau A, Locher C, Boussageon R, Laviolle B, et al. (2018): Pharmacologically controlled drinking in the treatment of alcohol dependence or alcohol use disorders: a systematic review with direct and network meta-analyses on nalmefene, naltrexone, acamprosate, baclofen and topiramate. Addiction, 113, 220--37

See Also

netrank, netmeta, netposet, hasse

Examples

# NOT RUN {
# Use depression dataset
#
data(Linde2015)

# Define order of treatments
#
trts <- c("TCA", "SSRI", "SNRI", "NRI",
          "Low-dose SARI", "NaSSa", "rMAO-A", "Hypericum",
          "Placebo")

# Outcome labels
#
outcomes <- c("Early response", "Early remission")

# (1) Early response
#
p1 <- pairwise(treat = list(treatment1, treatment2, treatment3),
               event = list(resp1, resp2, resp3),
               n = list(n1, n2, n3),
               studlab = id, data = Linde2015, sm = "OR")
#
net1 <- netmeta(p1, comb.fixed = FALSE,
                seq = trts, ref = "Placebo")

# (2) Early remission
#
p2 <- pairwise(treat = list(treatment1, treatment2, treatment3),
               event = list(remi1, remi2, remi3),
               n = list(n1, n2, n3),
               studlab = id, data = Linde2015, sm = "OR")
#
net2 <- netmeta(p2, comb.fixed = FALSE,
                seq = trts, ref = "Placebo")

# Image plot of treatment rankings (two outcomes)
#
plot(netrank(net1, small.values = "bad"),
     netrank(net2, small.values = "bad"),
     name = outcomes, digits = 2)


# Outcome labels
#
outcomes <- c("Early response", "Early remission",
              "Lost to follow-up", "Lost to follow-up due to AEs",
              "Adverse events (AEs)")

# (3) Loss to follow-up
#
p3 <- pairwise(treat = list(treatment1, treatment2, treatment3),
               event = list(loss1, loss2, loss3),
               n = list(n1, n2, n3),
               studlab = id, data = Linde2015, sm = "OR")
#
net3 <- netmeta(p3, comb.fixed = FALSE,
                seq = trts, ref = "Placebo")

# (4) Loss to follow-up due to adverse events
#
p4 <- pairwise(treat = list(treatment1, treatment2, treatment3),
               event = list(loss.ae1, loss.ae2, loss.ae3),
               n = list(n1, n2, n3),
               studlab = id, data = subset(Linde2015, id != 55),
               sm = "OR")
#
net4 <- netmeta(p4, comb.fixed = FALSE,
                seq = trts, ref = "Placebo")

# (5) Adverse events
#
p5 <- pairwise(treat = list(treatment1, treatment2, treatment3),
               event = list(ae1, ae2, ae3),
               n = list(n1, n2, n3),
               studlab = id, data = Linde2015, sm = "OR")
#
net5 <- netmeta(p5, comb.fixed = FALSE,
                seq = trts, ref = "Placebo")

# Image plot of treatment rankings (two outcomes)
#
plot(netrank(net1, small.values = "bad"),
     netrank(net2, small.values = "bad"),
     netrank(net3, small.values = "good"),
     netrank(net4, small.values = "good"),
     netrank(net5, small.values = "good"),
     name = outcomes, digits = 2)
# }
# NOT RUN {
# }