forest.subgroup.netmeta: Forest plot showing results of network meta-analysis with subgroups

Description

Forest plot to show subgroup estimates of network meta-analysis.

Usage

# S3 method for subgroup.netmeta
forest(
  x,
  pooled = ifelse(x$x$random, "random", "common"),
  equal.size = gs("equal.size"),
  leftcols = c("studlab", "Q", "df.Q", "pval.Q"),
  leftlabs = c("Comparison /\nSubgroup", "Q", "d.f.", "p-value"),
  rightcols = c("effect", "ci", "k", if (pooled == "random") "tau"),
  rightlabs = c(NA, NA, "Number of\nStudies", if (pooled == "random") "Tau"),
  calcwidth.subgroup = gs("calcwidth.subgroup"),
  digits = gs("digits.forest"),
  digits.Q = gs("digits.Q"),
  digits.pval.Q = gs("digits.pval.Q"),
  digits.tau2 = gs("digits.tau2"),
  digits.tau = gs("digits.tau"),
  sep.trts = " vs ",
  backtransf = x$x$backtransf,
  lab.NA = ".",
  smlab,
  col.subgroup = "black",
  ...
)
# S3 method for subgroup.netmeta
plot(x, ...)

Arguments

x: An object of class subgroup.netmeta.
pooled: A character string indicating whether results for the common ("common") or random effects model ("random") should be plotted. Can be abbreviated.
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 columns to be plotted on the left side of the forest plot (see Details).
leftlabs: A character vector specifying labels for columns on left side of the forest plot.
rightcols: A character vector specifying columns to be plotted on the right side of the forest plot (see Details).
rightlabs: A character vector specifying labels for columns on right side of the forest plot.
calcwidth.subgroup: A logical indicating whether text with comparison labels should be considered to calculate width of the column with treatment labels.
digits: Minimal number of significant digits for treatment effects and confidence intervals, see print.default.
digits.Q: Minimal number of significant digits for heterogeneity statistic Q, see print.default.
digits.pval.Q: Minimal number of significant digits for p-value of heterogeneity test, see print.default.
digits.tau2: Minimal number of significant digits for between-study variance \(\tau^2\), see print.default.
digits.tau: Minimal number of significant digits for \(\tau\), the square root of the between-study variance \(\tau^2\).
sep.trts: A character string used to label treatment comparisons.
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.
smlab: A label printed at top of figure. By default, text indicating either common or random effects model is printed.
col.subgroup: The colour to print information on subgroups.
...: Additional arguments for forest.meta function.

Author

Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de

Details

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

The arguments leftcols and rightcols can be used to specify columns which are plotted on the left and right side of the forest plot, respectively. If argument rightcols is FALSE, no columns will be plotted on the right side.

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

Examples

Run this code

if (FALSE) {
data("Senn2013")
# Add variable with (fictitious) risk of bias values
Senn2013$rob <- NA
set.seed(1909)
for (i in unique(Senn2013$studlab))
  Senn2013$rob[Senn2013$studlab == i] <- sample(1:3, 1)
Senn2013$rob <- factor(Senn2013$rob, levels = 1:3,
  labels = c("low", "moderate", "high"))
# Conduct network meta-analysis
net <- netmeta(TE, seTE, treat1.long, treat2.long, studlab,
  data = Senn2013, sm = "MD", reference = "plac", nchar.trts = 4)
# Conduct subgroup network meta-analysis
sg <- subgroup(net, rob, common = FALSE)
sg
# Forest plot
forest(sg)
}