Draws a forest plot in the active graphics window (using grid graphics system).
# S3 method for netmeta
forest(
x,
pooled = ifelse(x$random, "random", "common"),
reference.group = x$reference.group,
baseline.reference = x$baseline.reference,
labels = x$trts,
equal.size = gs("equal.size"),
leftcols = "studlab",
leftlabs,
rightcols = c("effect", "ci"),
rightlabs,
digits = gs("digits.forest"),
small.values = x$small.values,
nsim = gs("nsim"),
digits.prop = 2,
smlab = NULL,
sortvar = x$seq,
overall.hetstat = gs("overall.hetstat"),
print.tau2 = gs("forest.tau2"),
print.tau = gs("forest.tau"),
print.I2 = gs("forest.I2"),
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"),
col.subgroup = "black",
print.subgroup.name = FALSE,
...
)# S3 method for netmeta
plot(x, ...)
An object of class netmeta.
A character string indicating whether results for the
common ("common") or random effects model
("random") should be plotted. Can be abbreviated.
Reference treatment(s).
A logical indicating whether results should be expressed as comparisons of other treatments versus the reference treatment (default) or vice versa.
An optional vector with treatment labels.
A logical indicating whether all squares should be of equal size. Otherwise, the square size is proportional to the precision of estimates.
A character vector specifying columns to be plotted on the left side of the forest plot or a logical value (see Details).
A character vector specifying labels for (additional) columns on left side of the forest plot (see Details).
A character vector specifying columns to be plotted on the right side of the forest plot or a logical value (see Details).
A character vector specifying labels for (additional) columns on right side of the forest plot (see Details).
Minimal number of significant digits for treatment
effects and confidence intervals, see print.default.
A character string specifying whether small
treatment effects indicate a beneficial ("desirable") or
harmful ("undesirable") effect, can be abbreviated; see
netrank.
Number of simulations to calculate SUCRAs.
Minimal number of significant digits for
P-scores, SUCRAs and direct evidence proportions, see
print.default and netrank.
A label printed at top of figure. By default, text indicating either common or random effects model is printed.
An optional vector used to sort treatments (must be of same length as the total number of treatments).
A logical indicating whether to print heterogeneity measures.
A logical value indicating whether to print the value of the between-study variance \(\tau^2\).
A logical value indicating whether to print \(\tau\), the square root of the between-study variance \(\tau^2\).
A logical value indicating whether to print the value of the I-squared statistic.
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.
A character string to label missing values.
An optional data frame with additional columns to print in forest plot (see Details).
A numeric value indicating how many empty rows are printed between meta-analysis results and heterogeneity statistics.
A logical indicating whether the reference group should be printed in the forest plot.
The colour to print information on subgroups.
A logical indicating whether the name of the grouping variable should be printed in front of the group labels.
Additional arguments for forest.meta
function.
Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de
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 provide either sortvar = Pscore, sortvar =
"Pscore", sortvar = -Pscore, or sortvar = "-Pscore"
in order to sort treatments according to the ranking generated by
netrank which is called internally. It is also
possible to use "SUCRA" instead of "Pscore". Similar expressions
are possible to sort by treatment comparisons (sortvar = TE,
etc.), standard error (sortvar = seTE), number of studies
with direct treatment comparisons (sortvar = k), and direct
evidence proportion (sortvar = prop.direct, see also
netmeasures).
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 following columns are available:
| Name | Definition |
"studlab" | Treatments |
"TE" | Network estimates (either from common or random effects model) |
"seTE" | Corresponding standard errors |
"Pscore" | P-scores (see netrank) |
"SUCRA" | SUCRAs (see netrank) |
"n.trts" | Number of participants per treatment arm |
"k" | Number of studies in pairwise comparisons |
"prop.direct" | Direct evidence proportions (see
netmeasures) |
"effect" | (Back-transformed) network estimates |
"ci" | Confidence intervals |
"effect.ci" | (Back-transformed) network estimates and confidence intervals |
As a sidenote, the rather odd column name "studlab" to
describe the treatment comparisons comes from internally calling
forest.meta which uses study labels as the essential
information.
Argument add.data can be used to add additional columns to
the forest plot. This argument must be a data frame with row names
equal to the treatment names in R object x, i.e.,
x$trts.
See help page of forest.meta for more information on
the generation of forest plots and additional arguments.
forest.meta