forest.netcomb: Forest plot for additive network meta-analysis
In netmeta: Network Meta-Analysis using Frequentist Methods

forest.netcomb

R Documentation

Forest plot for additive network meta-analysis

Description

Draws a forest plot in the active graphics window (using grid graphics system).

Usage

## S3 method for class 'netcomb'
forest(
  x,
  pooled = ifelse(x$random, "random", "common"),
  reference.group = x$reference.group,
  baseline.reference = x$baseline.reference,
  equal.size = gs("equal.size"),
  leftcols = "studlab",
  leftlabs = "Treatment",
  rightcols = c("effect", "ci"),
  rightlabs = NULL,
  digits = gs("digits.forest"),
  smlab = NULL,
  sortvar = x$seq,
  overall.hetstat = gs("overall.hetstat"),
  backtransf = x$backtransf,
  lab.NA = gs("lab.NA"),
  add.data,
  addrows.below.overall = if (x$overall.hetstat) 2 else gs("addrows.below.overall"),
  drop.reference.group = gs("drop.reference.group"),
  ...
)

## S3 method for class 'netcomb'
plot(x, ...)

Arguments

`x`	An object of class `netcomb`.
`pooled`	A character string indicating whether results for the common (`"common"`) or random effects model (`"random"`) should be plotted. Can be abbreviated.
`reference.group`	Reference treatment(s).
`baseline.reference`	A logical indicating whether results should be expressed as comparisons of other treatments versus the reference treatment (default) or vice versa.
`equal.size`	A logical indicating whether all squares should be of equal size. Otherwise, the square size is proportional to the precision of estimates.
`leftcols`	A character vector specifying (additional) columns to be plotted on the left side of the forest plot or a logical value (see `forest.meta` help page for details).
`leftlabs`	A character vector specifying labels for (additional) columns on left side of the forest plot (see `forest.meta` help page for details).
`rightcols`	A character vector specifying (additional) columns to be plotted on the right side of the forest plot or a logical value (see `forest.meta` help page for details).
`rightlabs`	A character vector specifying labels for (additional) columns on right side of the forest plot (see `forest.meta` help page for details).
`digits`	Minimal number of significant digits for treatment effects and confidence intervals, see `print.default`.
`smlab`	A label printed at top of figure. By default, text indicating either common or random effects model is printed.
`sortvar`	An optional vector used to sort the individual studies (must be of same length as the total number of treatments).
`overall.hetstat`	A logical indicating whether to print heterogeneity measures.
`backtransf`	A logical indicating whether results should be back transformed in forest plots. If `backtransf = TRUE`, results for `sm = "OR"` are presented as odds ratios rather than log odds ratios, for example.
`lab.NA`	A character string to label missing values.
`add.data`	An optional data frame with additional columns to print in forest plot (see Details).
`addrows.below.overall`	A numeric value indicating how many empty rows are printed between meta-analysis results and heterogeneity statistics.
`drop.reference.group`	A logical indicating whether the reference group should be printed in the forest plot.
`...`	Additional arguments for `forest.meta` function.

Details

A forest plot, also called confidence interval plot, is drawn in the active graphics window.

Argument sortvar can be either a numeric or character vector with length of number of treatments. If sortvar is numeric the order function is utilised internally to determine the order of values. If sortvar is character it must be a permutation of the treatment names. It is also possible to to sort by treatment comparisons (sortvar = TE, etc.), standard error (sortvar = seTE), and number of studies with direct treatment comparisons (sortvar = k).

Argument add.data can be used to add additional columns to the forest plot. This argument must be a data frame with the same row names as the treatment effects matrices in R object x, i.e., x$TE.common or x$TE.random.

For more information see help page of forest.meta function.

Author(s)

Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de

Examples


data(Linde2016)
# Specify, order of treatments
#
trts <- c("TCA", "SSRI", "SNRI", "NRI", "Low-dose SARI", "NaSSa",
  "rMAO-A", "Ind drug", "Hypericum", "Face-to-face CBT",
  "Face-to-face PST", "Face-to-face interpsy", "Face-to-face psychodyn",
  "Other face-to-face", "Remote CBT", "Self-help CBT", "No contact CBT",
  "Face-to-face CBT + SSRI", "Face-to-face interpsy + SSRI",
  "Face-to-face PST + SSRI", "UC", "Placebo")
#
# Note, three treatments are actually combinations of 'SSRI' with
# other components:
# "Face-to-face CBT + SSRI",
# "Face-to-face interpsy + SSRI",
# "Face-to-face PST + SSRI"

# Conduct random effects network meta-analysis
#
net1 <- netmeta(lnOR, selnOR, treat1, treat2, id,
  data = Linde2016, ref = "placebo",
  seq = trts, sm = "OR", common = FALSE)
#
net1

# Additive model for treatment components (with placebo as inactive
# treatment)
#
nc1 <- netcomb(net1, inactive = "placebo")
#
forest(nc1)

netmeta documentation built on April 3, 2025, 6:12 p.m.