Plot treatment ranking(s) of network meta-analyses
Produce an image plot of treatment ranking(s) generated with R
function netrank.
## S3 method for class '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 )
... |
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 |
mid |
A character string defining the colour for a P-score of
0.5, see |
high |
A character string defining the colour for a P-score of
1, see |
col |
Colour of text. |
main |
Title. |
main.size |
Font size of title, see
|
main.col |
Colour of title, see
|
main.face |
Font face of title, see
|
legend |
A logical indicating whether a legend should be printed. |
axis.size |
Font size of axis text, see
|
axis.col |
Colour of axis text, see
|
axis.face |
Font face of axis text, see
|
na.value |
Colour for missing values, see
|
angle |
Angle for text on x-axis, see
|
hjust.x |
A numeric between 0 and 1 with horizontal
justification of text on x-axis, see
|
vjust.x |
A numeric between 0 and 1 with vertical
justification of text on x-axis, see
|
hjust.y |
A numeric between 0 and 1 with horizontal
justification of text on y-axis, see
|
vjust.y |
A numeric between 0 and 1 with vertical
justification of text on y-axis, see
|
nchar.trts |
A numeric defining the minimum number of characters used to create unique treatment names. |
digits |
Minimal number of significant digits, see
|
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.
A ggplot2 object.
Guido Schwarzer sc@imbi.uni-freiburg.de, Clément Palpacuer clementpalpacuer@gmail.com
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
## 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)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.