summary.netcomb: Summary method for objects of class netcomb

In netmeta: Network Meta-Analysis using Frequentist Methods

Summary method for objects of class netcomb

Description

Summary method for objects of class netcomb.

Usage

## S3 method for class 'netcomb'
summary(
  object,
  common = object$common,
  random = object$random,
  overall.hetstat = object$overall.hetstat,
  backtransf = object$backtransf,
  nchar.comps = object$nchar.comps,
  warn.deprecated = gs("warn.deprecated"),
  ...
)

Arguments

object	An object of class netcomb.
common	A logical indicating whether results for the common effects model should be printed.
random	A logical indicating whether results for the random effects model should be printed.
overall.hetstat	A logical indicating whether to print heterogeneity measures.
backtransf	A logical indicating whether results should be back transformed in printouts and forest plots.
nchar.comps	A numeric defining the minimum number of characters used to create unique component names.
warn.deprecated	A logical indicating whether warnings should be printed if deprecated arguments are used.
...	Additional arguments (to catch deprecated arguments).

Value

A list of class "summary.netcomb" is returned with the following elements:

k	Total number of studies.
m	Total number of pairwise comparisons.
n	Total number of treatments.
d	Total number of designs (corresponding to the unique set of treatments compared within studies).
c	Total number of components.
s	Total number of subnetworks (for discomb).
trts	Treatments included in network meta-analysis.
k.trts	Number of studies evaluating a treatment.
n.trts	Number of observations receiving a treatment (if available).
events.trts	Number of events observed for a treatment (if available).
studies	Study labels coerced into a factor with its levels sorted alphabetically.
narms	Number of arms for each study.
designs	Vector with unique designs present in the network. A design corresponds to the set of treatments compared within a study.
comps	Components included in network meta-analysis.
k.comps	Number of studies evaluating a component.
n.comps	Number of observations receiving a component (if available).
events.comps	Number of events observed for a component (if available).
comparison	Results for pairwise comparisons (data frame with columns studlab, treat1, treat2, TE, seTE, lower, upper, z, p).
comparison.nma.common	Results for pairwise comparisons based on common effects NMA model (data frame with columns studlab, treat1, treat2, TE, seTE, lower, upper, z, p, leverage).
comparison.nma.random	Results for pairwise comparisons based on random effects NMA model (data frame with columns studlab, treat1, treat2, TE, seTE, lower, upper, z, p).
comparison.cnma.common	Results for pairwise comparisons based on common effects CNMA model (data frame with columns studlab, treat1, treat2, TE, seTE, lower, upper, z, p, leverage).
comparison.cnma.random	Results for pairwise comparisons based on random effects CNMA model (data frame with columns studlab, treat1, treat2, TE, seTE, lower, upper, z, p).
components.common	Results for components based on common effects CNMA model (data frame with columns studlab, treat1, treat2, TE, seTE, lower, upper, z, p, leverage).
components.random	Results for components based on random effects CNMA model (data frame with columns studlab, treat1, treat2, TE, seTE, lower, upper, z, p).
combinations.common	Results for available combinations based on common effects CNMA model (data frame with columns studlab, treat1, treat2, TE, seTE, lower, upper, z, p, leverage).
combinations.random	Results for available combinations based on random effects CNMA model (data frame with columns studlab, treat1, treat2, TE, seTE, lower, upper, z, p).
common	Results for common effects model (a list with elements TE, seTE, lower, upper, z, p).
random	Results for random effects model (a list with elements TE, seTE, lower, upper, z, p).
predict	Prediction intervals (a list with elements seTE, lower, upper).
Q.additive	Overall heterogeneity / inconsistency statistic.
df.Q.additive	Degrees of freedom for test of heterogeneity / inconsistency.
pval.Q.additive	P-value for test of heterogeneity / inconsistency.
I2, lower.I2, upper.I2	I-squared, lower and upper confidence limits.
tau	Square-root of between-study variance.
Q.additive	Overall heterogeneity / inconsistency statistic (CNMA model).
df.Q.additive	Degrees of freedom for test of heterogeneity / inconsistency (CNMA model).
pval.Q.additive	P-value for test of heterogeneity / inconsistency (CNMA model).
Q.standard	Overall heterogeneity statistic (NMA model).
df.Q.heterogeneity	Degrees of freedom for test of overall heterogeneity (NMA model).
pval.Q.heterogeneity	P-value for test of overall heterogeneity (NMA model).
Q.diff	Q statistic for difference between CNMA and NMA model.
df.Q.diff, pval.Q.diff	Corresponding degrees of freedom and p-value.
sm	A character string indicating underlying summary measure.
method	A character string indicating which method is to be used for pooling of studies.
level	The level used to calculate confidence intervals for individual studies.
level.ma	The level used to calculate confidence intervals for pooled estimates.
ci.lab	Label for confidence interval.
reference.group, baseline.reference	As defined above.
all.treatments	As defined above.
seq	A character specifying the sequence of treatments.
tau.preset	An optional value for the square-root of the between-study variance \tau^2.
sep.comps	A character used in comparison names as separator between component labels.
nchar.comps	A numeric defining the minimum number of characters used to create unique component names.
overall.hetstat, backtransf	As defined above.
title	Title of meta-analysis / systematic review.
x	netmeta object (if available).
call	Function call.
version	Version of R package netmeta used to create object.

Author(s)

Guido Schwarzer [e-mail]

See Also

netcomb, discomb

Examples

data(Linde2016)

# Only consider studies including Face-to-face PST (to reduce
# runtime of example)
#
face <- subset(Linde2016, id %in% c(16, 24, 49, 118))

# Conduct random effects network meta-analysis
#
net1 <- netmeta(lnOR, selnOR, treat1, treat2, id,
  data = face, reference.group = "placebo",
  sm = "OR", common = FALSE)

# Additive model for treatment components
#
nc1 <- netcomb(net1)
summary(nc1)


print(summary(nc1), digits = 2, digits.stat = 3)

# Conduct random effects network meta-analysis
#
net2 <- netmeta(lnOR, selnOR, treat1, treat2, id,
  data = Linde2016, reference.group = "placebo",
  sm = "OR", common = FALSE)

# Additive model for treatment components
#
nc2 <- netcomb(net2)
summary(nc2)
print(summary(nc2), digits = 2, digits.stat = 3)

netmeta documentation built on April 3, 2025, 6:12 p.m.