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

In netmeta: Network Meta-Analysis using Frequentist Methods

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 class 'netrank'
plot(
  ...,
  name,
  common,
  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,
  fixed,
  comb.fixed,
  comb.random,
  warn.deprecated = gs("warn.deprecated")
)

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.
common	A logical indicating whether results for the common effects model should be plotted.
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.
fixed	Deprecated argument (replaced by 'common').
comb.fixed	Deprecated argument (replaced by 'common').
comb.random	Deprecated argument (replaced by 'random').
warn.deprecated	A logical indicating whether warnings should be printed if deprecated arguments are used.

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.

Value

A ggplot2 object or NULL if no ranking was conducted.

Author(s)

Guido Schwarzer [e-mail], Clément Palpacuer [e-mail]

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, common = 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, common = FALSE,
  seq = trts, ref = "Placebo")

# Image plot of treatment rankings (two outcomes)
#
plot(netrank(net1, small.values = "undesirable"),
  netrank(net2, small.values = "undesirable"),
  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, common = 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, common = 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, common = FALSE, seq = trts, ref = "Placebo")

# Image plot of treatment rankings (two outcomes)
#
plot(netrank(net1, small.values = "undesirable"),
  netrank(net2, small.values = "undesirable"),
  netrank(net3, small.values = "desirable"),
  netrank(net4, small.values = "desirable"),
  netrank(net5, small.values = "desirable"),
  name = outcomes, digits = 2)

## End(Not run)

netmeta documentation built on May 31, 2023, 5:45 p.m.