netmeta (version 1.5-0)

print.netmeta: Print results of network meta-analysis

Description

Print method for objects of class netmeta.

Usage

# S3 method for netmeta
print(
  x,
  sortvar,
  comb.fixed = x$comb.fixed,
  comb.random = x$comb.random,
  prediction = x$prediction,
  reference.group = x$reference.group,
  baseline.reference = x$baseline.reference,
  all.treatments = x$all.treatments,
  details = TRUE,
  ma = TRUE,
  backtransf = x$backtransf,
  nchar.trts = x$nchar.trts,
  digits = gs("digits"),
  digits.se = gs("digits.se"),
  digits.pval.Q = max(gs("digits.pval.Q"), 2),
  digits.Q = gs("digits.Q"),
  digits.tau2 = gs("digits.tau2"),
  digits.I2 = gs("digits.I2"),
  scientific.pval = gs("scientific.pval"),
  big.mark = gs("big.mark"),
  truncate,
  text.truncate = "*** Output truncated ***",
  legend = TRUE,
  ...
)

Arguments

x

An object of class netmeta.

sortvar

An optional vector used to sort individual studies (must be of same length as x$TE).

comb.fixed

A logical indicating whether results for the fixed effects (common effects) model should be printed.

comb.random

A logical indicating whether results for the random effects model should be printed.

prediction

A logical indicating whether prediction intervals should be printed.

reference.group

Reference treatment.

baseline.reference

A logical indicating whether results should be expressed as comparisons of other treatments versus the reference treatment (default) or vice versa. This argument is only considered if reference.group has been specified.

all.treatments

A logical or "NULL". If TRUE, matrices with all treatment effects, and confidence limits will be printed.

details

A logical indicating whether further details for individual studies should be printed.

ma

A logical indicating whether summary results of meta-analysis should be printed.

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.

digits

Minimal number of significant digits, see print.default.

digits.se

Minimal number of significant digits for standard deviations and standard errors, see print.default.

digits.pval.Q

Minimal number of significant digits for p-value of heterogeneity tests, see print.default.

digits.Q

Minimal number of significant digits for heterogeneity statistics, see print.default.

digits.tau2

Minimal number of significant digits for 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.

truncate

An optional vector used to truncate the printout of results for individual studies (must be a logical vector of same length as x$TE or contain numerical values).

text.truncate

A character string printed if study results were truncated from the printout.

legend

A logical indicating whether a legend should be printed.

…

Additional arguments.

See Also

netmeta

Examples

# NOT RUN {
data(Senn2013)

# Conduct fixed effects network meta-analysis
#
net1 <- netmeta(TE, seTE, treat1, treat2, studlab,
                data = Senn2013, sm = "MD",
                comb.random = FALSE)
print(net1, ref = "plac", digits = 3)

# Only show individual study results for multi-arm studies
#
print(net1, ref = "plac", digits = 3, truncate = multiarm)

# Only show first three individual study results
#
print(net1, ref = "plac", digits = 3, truncate = 1:3)

# Only show individual study results for Kim2007 and Willms1999
#
print(net1, ref = "plac", digits = 3,
      truncate = c("Kim2007", "Willms1999"))

# Only show individual study results for studies starting with the
# letter "W"
#
print(net1, ref = "plac", digits = 3,
      truncate = substring(studlab, 1, 1) == "W")

# }
# NOT RUN {
# Conduct random effects network meta-analysis
#
net2 <- netmeta(TE, seTE, treat1, treat2, studlab,
                data = Senn2013, sm = "MD",
                comb.fixed = FALSE)
print(net2, ref = "plac", digits = 3)
# }
# NOT RUN {
# }