netmeta (version 3.1-1)

subgroup.netmeta: Subgroup analysis for network meta-analysis

Description

Subgroup analysis for objects of class netmeta.

Usage

# S3 method for netmeta
subgroup(
  x,
  subgroup,
  only.connected = FALSE,
  common = x$common,
  random = x$random,
  method.tau = x$method.tau,
  level.ma = x$level.ma,
  backtransf = x$backtransf,
  nchar.trts = x$nchar.trts,
  ...
)
subgroup(x, ...)
# S3 method for subgroup.netmeta
print(
  x,
  common = x$x$common,
  random = x$x$random,
  backtransf = x$x$backtransf,
  nchar.trts = x$x$nchar.trts,
  digits = gs("digits"),
  digits.se = gs("digits.se"),
  digits.Q = gs("digits.Q"),
  digits.pval.Q = gs("digits.pval.Q"),
  digits.tau2 = gs("digits.tau2"),
  digits.tau = gs("digits.tau"),
  big.mark = gs("big.mark"),
  scientific.pval = gs("scientific.pval"),
  zero.pval = gs("zero.pval"),
  JAMA.pval = gs("JAMA.pval"),
  print.se = !backtransf,
  print.tau2 = gs("print.tau2"),
  print.tau = gs("print.tau"),
  print.Q = gs("print.Q"),
  text.tau2 = gs("text.tau2"),
  text.tau = gs("text.tau"),
  details.methods = gs("details"),
  legend = gs("legend"),
  ...
)

Value

An object of class "subgroup.netmeta" with corresponding print

and forest function.

Arguments

x

An object of class netmeta (or subgroup.netmeta).

subgroup

A vector defining the subgroups considered in the network meta-analysis.

only.connected

A logical indicating whether networks of subgroups must be connected.

common

A logical indicating whether results for common effect subgroup network meta-analysis should be printed.

random

A logical indicating whether results for random effects subgroup network meta-analysis should be printed.

method.tau

A character string indicating which method is used to estimate the between-study variance \(\tau^2\) and its square root \(\tau\). Either "DL", "REML", or "ML", can be abbreviated.

level.ma

The level used to calculate confidence intervals for network estimates.

backtransf

A logical indicating whether results should be back transformed in printouts and forest plots. If backtransf = TRUE, results for sm = "OR" are presented as odds ratios rather than log odds ratios, for example.

nchar.trts

A numeric defining the minimum number of characters used to create unique treatment names (see netmeta).

...

Additional arguments.

digits

Minimal number of significant digits, see print.default.

digits.se

Minimal number of significant digits for standard errors.

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 \(\tau^2\).

big.mark

A character used as thousands separator.

scientific.pval

A logical specifying whether p-values should be printed in scientific notation, e.g., 1.2345e-01 instead of 0.12345.

zero.pval

A logical specifying whether p-values should be printed with a leading zero.

JAMA.pval

A logical specifying whether p-values for test of overall effect should be printed according to JAMA reporting standards.

print.se

A logical specifying whether standard errors should be printed.

print.tau2

A logical specifying whether between-study variance \(\tau^2\) should be printed.

print.tau

A logical specifying whether \(\tau\), the square root of the between-study variance \(\tau^2\), should be printed.

print.Q

A logical value indicating whether to print the results of the test of heterogeneity.

text.tau2

Text printed to identify between-study variance \(\tau^2\).

text.tau

Text printed to identify \(\tau\), the square root of the between-study variance \(\tau^2\).

details.methods

A logical specifying whether details on statistical methods should be printed.

legend

A logical indicating whether a legend should be printed.

See Also

forest.subgroup.netmeta, forest.netmeta

Examples

# \donttest{
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
subgroup(net, rob, common = FALSE)
# }