mtrank (version 0.2-0)

forest.tcc: Forest plot showing the treatment preference format of the NMA estimates according to treatment choice criterion.

Description

This function produces a forest plot and visualizes the treatment preference format of the NMA estimates as defined from the treatment choice criterion in tcc.

Usage

# S3 method for tcc
forest(
  x,
  reference.group = x$reference.group,
  baseline.reference = x$baseline.reference,
  backtransf = FALSE,
  leftcols = "studlab",
  leftlabs,
  rightcols = c("effect", "ci"),
  col.winner = "red",
  col.tie = "black",
  lty.equi = gs("lty.cid"),
  col.equi = gs("col.cid"),
  fill.equi = "lightblue",
  fill.swd.below.null = "transparent",
  fill.swd.above.null = "transparent",
  smlab,
  header.line = TRUE,
  ...
)

Value

A forest plot is plotted in the active graphics device.

Arguments

x

An object of class tcc.

reference.group

Reference treatment(s). By default, the graph plots the NMA estimates of all treatments versus the common reference treatment used in the netmeta object.

baseline.reference

A logical indicating whether results should be expressed as comparisons of other treatments versus the reference treatment (default) or vice versa.

backtransf

A logical indicating whether results should be back transformed. If backtransf = TRUE (default), results for sm = "OR" are printed as odds ratios rather than log odds ratios, for example.

leftcols

A character vector specifying columns to be printed on the left side of the forest plot (see forest.meta).

leftlabs

A character vector specifying labels for columns on left side of the forest plot.

rightcols

A character vector specifying columns to be printed on the right side of the forest plot (see forest.meta).

col.winner

Colour to highlight results for TCC winner.

col.tie

Colour to highlight results for TCC ties.

lty.equi

Line type (limits of equivalence).

col.equi

Line colour (limits of equivalence).

fill.equi

Colour(s) for area between limits of equivalence.

fill.swd.below.null

Colour of area below lower SWD limit.

fill.swd.above.null

Colour of area above upper SWD limit.

smlab

A label for the summary measure (printed at top of figure).

header.line

A logical value indicating whether to print a header line or a character string ("both", "below", "").

...

Additional arguments (passed on to forest.meta).

Details

This function produces forest plots for the NMA treatment effect estimates. The color indicates whether treatment effects show a preference (red color) or tie (black color). Additionally, the respective range of equivalence defined at the function tcc is visualized for the forest plot.

The argument reference.group is optional. By default, the graph plots the NMA estimates of all treatments versus the common reference treatment used in the netmeta object.

References

Evrenoglou T, Nikolakopoulou A, Schwarzer G, Rücker 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")
#
pw1 <- pairwise(studlab = studyid, treat = drug_name,
  n = ntotal, event = responders,
  data = antidepressants, sm = "OR")
# Use subset to reduce runtime
pw0 <- subset(pw1, studyid < 60)
#
net0 <- netmeta(pw0, reference.group = "tra")

ranks0 <- tcc(net0, swd = 1.20, small.values = "undesirable")

# Comparison other drugs vs trazodone
forest(ranks0,
  label.left = "Favours trazodone",
  label.right = "Favours other drug")

# Comparison escitalopram vs other drugs
forest(ranks0, reference.group = "esc", baseline = FALSE,
  label.left = "Favours other drug",
  label.right = "Favours escitalopram")

if (FALSE) {
# Store a PDF file in the current working directory showing all results
# (this is the default, i.e., if argument 'reference.group' is missing)
forest(ranks0, baseline = FALSE, reference.group = trts,
  file = "forest_tcc_antidepressants.pdf")

# Run analysis with full data set
net1 <- netmeta(pw1, reference.group = "tra")

ranks1 <- tcc(net1, swd = 1.20, small.values = "undesirable")

# Comparison other drugs vs trazodone
forest(ranks1,
  label.left = "Favours trazodone",
  label.right = "Favours other drug")

# Comparison escitalopram vs other drugs
forest(ranks1, reference.group = "esc", baseline = FALSE,
  label.left = "Favours other drug",
  label.right = "Favours escitalopram")
}