linegraph {mtrank} | R Documentation |
Line graph showing the results of mtrank
across different
smallest worthwhile difference (SWD) values
Description
This function produces a line graph that visualizes the results of
mtrank
in terms of either abilities or probabilities across
different smallest worthwhile difference (SWD) values.
Usage
linegraph(
x,
swd,
swd.ref,
small.values = x$small.values,
type = "probability",
k = length(x$trts),
backtransf = FALSE,
linewidth = 1.1,
point.size = 2,
...
)
Arguments
x |
An object of class |
swd |
A numeric vector of SWD values to be used for the sensitivity analysis. |
swd.ref |
A numeric SWD value to be used as the reference for sorting
treatments in the final graph. This value must be included in
|
small.values |
A character string specifying whether small treatment
effects indicate a beneficial ( |
type |
The metric to be used for plotting the results of the
sensitivity analysis. Two options are available: the default is
|
k |
A numeric value indicating the number of treatments to be plotted.
By default, all available treatments are shown. For large networks, it is
advisable to limit the number of treatments to improve readability.
If specified, the first |
backtransf |
A logical value indicating whether to display log-ability
estimates ( |
linewidth |
A numeric value specifying the width of the lines (default: 1.1). |
point.size |
A numeric value specifying the size of the points (default: 2). |
... |
Additional arguments passed to |
Details
This function creates a line graph to visualize probability or ability
estimates obtained from mtrank
across different SWD values.
The order of treatments in the graph is based on their hierarchy at the
reference SWD value (swd.ref
).
Value
A ggplot
object.
References
Evrenoglou T, Nikolakopoulou A, Schwarzer G, Ruecker G, Chaimani A (2024): Producing treatment hierarchies in network meta-analysis using probabilistic models and treatment-choice criteria, https://arxiv.org/abs/2406.10612
Examples
data("antidepressants")
#
pw <- pairwise(studlab = studyid, treat = drug_name,
n = ntotal, event = responders,
data = antidepressants, sm = "OR")
# Use subset to reduce runtime
pw <- subset(pw, studyid < 60)
#
net <- netmeta(pw, reference.group = "tra")
#
ranks <- tcc(net, swd = 1.20, small.values = "undesirable")
#
fit <- mtrank(ranks)
#
# Perform a sensitivity analysis across different SWD values assuming that
# 1.20 is the reference value
swd.vec <- seq(1.10, 1.50, by = 0.10)
swd.ref <- 1.20
# plot all the treatments in the network
linegraph(fit, swd = swd.vec, swd.ref = swd.ref)
# plot only the first three treatments in the order appearing at the
# 'swd.ref' value
linegraph(fit, swd = swd.vec, swd.ref = swd.ref, k = 3)
# plot in terms of ability estimates
linegraph(fit, swd = swd.vec, swd.ref = swd.ref, type = "ability")