forest.metabind: Forest plot to display the result of a meta-analysis

In meta: General Package for Meta-Analysis

Forest plot to display the result of a meta-analysis

Description

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

Usage

## S3 method for class 'metabind'
forest(
  x,
  leftcols,
  leftlabs,
  rightcols = c("effect", "ci"),
  rightlabs,
  common = x$common,
  random = x$random,
  overall = x$overall,
  subgroup = FALSE,
  hetstat = FALSE,
  overall.hetstat = x$overall.hetstat,
  prediction = x$prediction,
  lab.NA = "",
  col.square = gs("col.square"),
  col.square.lines = col.square,
  col.circle = gs("col.circle"),
  col.circle.lines = col.circle,
  col.diamond = gs("col.diamond"),
  col.diamond.common = col.diamond,
  col.diamond.random = col.diamond,
  col.diamond.lines = gs("col.diamond.lines"),
  col.diamond.lines.common = col.diamond.lines,
  col.diamond.lines.random = col.diamond.lines,
  col.predict = gs("col.predict"),
  col.predict.lines = gs("col.predict.lines"),
  type = NULL,
  type.common = NULL,
  type.random = NULL,
  type.predict = NULL,
  digits = gs("digits.forest"),
  digits.se = gs("digits.se"),
  digits.stat = gs("digits.stat"),
  digits.pval = max(gs("digits.pval") - 2, 2),
  digits.pval.Q = max(gs("digits.pval.Q") - 2, 2),
  digits.Q = gs("digits.Q"),
  digits.tau2 = gs("digits.tau2"),
  digits.tau = gs("digits.tau"),
  digits.I2 = max(gs("digits.I2") - 1, 0),
  scientific.pval = gs("scientific.pval"),
  big.mark = gs("big.mark"),
  print.subgroup.labels = x$with.subgroups,
  addrow.subgroups = print.subgroup.labels,
  smlab,
  calcwidth.pooled = overall,
  warn.deprecated = gs("warn.deprecated"),
  ...
)

Arguments

x	An object of class metabind.
leftcols	A character vector specifying (additional) columns to be plotted on the left side of the forest plot or a logical value (see Details).
leftlabs	A character vector specifying labels for (additional) columns on left side of the forest plot (see Details).
rightcols	A character vector specifying (additional) columns to be plotted on the right side of the forest plot or a logical value (see Details).
rightlabs	A character vector specifying labels for (additional) columns on right side of the forest plot (see Details).
common	A logical indicating whether common effect estimates should be plotted.
random	A logical indicating whether random effects estimates should be plotted.
overall	A logical indicating whether overall summaries should be plotted. This argument is useful in a meta-analysis with subgroups if summaries should only be plotted on group level.
subgroup	A logical indicating whether subgroup results should be shown in forest plot. This argument is useful in a meta-analysis with subgroups if summaries should not be plotted on group level.
hetstat	Either a logical value indicating whether to print results for heterogeneity measures at all or a character string (see Details).
overall.hetstat	A logical value indicating whether to print heterogeneity measures for overall treatment comparisons. This argument is useful in a meta-analysis with subgroups if heterogeneity statistics should only be printed on subgroup level.
prediction	A logical indicating whether prediction interval(s) should be printed.
lab.NA	A character string to label missing values.
col.square	The colour for squares reflecting study's weight in the meta-analysis.
col.square.lines	The colour for the outer lines of squares reflecting study's weight in the meta-analysis.
col.circle	The colour for circles reflecting study weights in the meta-analysis.
col.circle.lines	The colour for the outer lines of circles reflecting study's weight in the meta-analysis.
col.diamond	The colour of diamonds representing the results for common effect and random effects models.
col.diamond.common	The colour of diamonds for common effect estimates.
col.diamond.random	The colour of diamonds for random effects estimates.
col.diamond.lines	The colour of the outer lines of diamonds representing the results for common effect and random effects models.
col.diamond.lines.common	The colour of the outer lines of diamond for common effect estimates.
col.diamond.lines.random	The colour of the outer lines of diamond for random effects estimates.
col.predict	Background colour of prediction intervals.
col.predict.lines	Colour of outer lines of prediction intervals.
type	A character string or vector specifying how to plot estimates.
type.common	A single character string specifying how to plot common effect estimates.
type.random	A single character string specifying how to plot random effects estimates.
type.predict	A single character string specifying how to plot prediction intervals.
digits	Minimal number of significant digits for treatment effects, see print.default.
digits.se	Minimal number of significant digits for standard errors, see print.default.
digits.stat	Minimal number of significant digits for z- or t-statistic for test of overall effect, see print.default.
digits.pval	Minimal number of significant digits for p-value of overall treatment effect, see print.default.
digits.pval.Q	Minimal number of significant digits for p-value of heterogeneity test, see print.default.
digits.Q	Minimal number of significant digits for heterogeneity statistic Q, see print.default.
digits.tau2	Minimal number of significant digits for between-study variance, see print.default.
digits.tau	Minimal number of significant digits for square root of between-study variance, see print.default.
digits.I2	Minimal number of significant digits for I-squared statistic, see print.default.
scientific.pval	A logical specifying whether p-values should be printed in scientific notation, e.g., 1.2345e-01 instead of 0.12345.
big.mark	A character used as thousands separator.
print.subgroup.labels	A logical indicating whether subgroup label should be printed.
addrow.subgroups	A logical value indicating whether an empty row is printed between results for subgroups.
smlab	A label for the summary measurex (printed at top of figure).
calcwidth.pooled	A logical indicating whether text for common effect and random effects model should be considered to calculate width of the column with study labels.
warn.deprecated	A logical indicating whether warnings should be printed if deprecated arguments are used.
...	Additional graphical arguments (passed on to forest.meta).

Details

A forest plot, also called confidence interval plot, is drawn in the active graphics window. The forest functions in R package meta are based on the grid graphics system. In order to print the forest plot, resize the graphics window and either use dev.copy2eps or dev.copy2pdf. Another possibility is to create a file using pdf, png, or svg and to specify the width and height of the graphic (see forest.meta examples).

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.

The arguments leftlabs and rightlabs can be used to specify column headings which are plotted on left and right side of the forest plot, respectively. For certain columns predefined labels exist. For other columns, the column name will be used as a label. It is possible to only provide labels for new columns (see forest.meta examples). Otherwise the length of leftlabs and rightlabs must be the same as the number of printed columns, respectively. The value NA can be used to specify columns which should use default labels.

Argument hetstat can be a character string to specify where to print heterogeneity information:

• row with results for common effect model (hetstat = "common"),

• row with results for random effects model (hetstat = "random"),

• rows with 'study' information (hetstat = "study").

Otherwise, information on heterogeneity is printed in dedicated rows.

Author(s)

Guido Schwarzer [e-mail]

See Also

forest.meta, metabin, metacont, metagen, metabind, settings.meta

Examples

data(Fleiss1993cont)

# Add some (fictitious) grouping variables:
#
Fleiss1993cont$age <- c(55, 65, 55, 65, 55)
Fleiss1993cont$region <- c("Europe", "Europe", "Asia", "Asia", "Europe")

m1 <- metacont(n.psyc, mean.psyc, sd.psyc, n.cont, mean.cont, sd.cont,
  data = Fleiss1993cont, sm = "SMD")

# Conduct two subgroup analyses
#
mu1 <- update(m1, subgroup = age, bylab = "Age group")
mu2 <- update(m1, subgroup = region, bylab = "Region")

# Combine subgroup meta-analyses and show forest plot with subgroup
# results
#
mb1 <- metabind(mu1, mu2)
mb1
forest(mb1)

meta documentation built on Oct. 31, 2024, 5:07 p.m.