summary.netmeta: Summary method for objects of class netmeta

In netmeta: Network Meta-Analysis using Frequentist Methods

Summary method for objects of class netmeta

Description

Summary method for objects of class netmeta.

Usage

## S3 method for class 'netmeta'
summary(
  object,
  common = object$common,
  random = object$random,
  prediction = object$prediction,
  reference.group = object$reference.group,
  baseline.reference = object$baseline.reference,
  all.treatments = object$all.treatments,
  backtransf = object$backtransf,
  nchar.trts = object$nchar.trts,
  warn.deprecated = gs("warn.deprecated"),
  ...
)

Arguments

object	An object of class netmeta.
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.
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.
backtransf	A logical indicating whether results should be back transformed in printouts and forest plots.
nchar.trts	A numeric defining the minimum number of characters used to create unique treatment names (see Details).
warn.deprecated	A logical indicating whether warnings should be printed if deprecated arguments are used.
...	Additional arguments (to catch deprecated arguments).

Value

A list is returned with the following elements:

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 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 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).
studies	Study labels coerced into a factor with its levels sorted alphabetically.
narms	Number of arms for each study.
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).
Q	Overall heterogeneity / inconsistency statistic.
df.Q	Degrees of freedom for test of heterogeneity / inconsistency.
pval.Q	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.heterogeneity	Overall heterogeneity statistic.
df.Q.heterogeneity	Degrees of freedom for test of overall heterogeneity.
pval.Q.heterogeneity	P-value for test of overall heterogeneity.
Q.inconsistency	Overall inconsistency statistic.
df.Q.inconsistency	Degrees of freedom for test of overall inconsistency.
pval.Q.inconsistency	P-value for test of overall inconsistency.
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.
common, random	As defined above.
prediction, level.predict	As defined above.
reference.group, baseline.reference	As defined above.
all.treatments, backtransf	As defined above.
ci.lab	Label for confidence interval.
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.trts	A character used in comparison names as separator between treatment labels.
nchar.trts	A numeric defining the minimum number of characters used to create unique treatment names.
title	Title of meta-analysis / systematic review.
call	Function call.
version	Version of R package netmeta used to create object.

Author(s)

Guido Schwarzer [e-mail]

See Also

netmeta

Examples

data(smokingcessation)

p1 <- pairwise(list(treat1, treat2, treat3),
  event = list(event1, event2, event3), n = list(n1, n2, n3),
  data = smokingcessation, sm = "OR")
net1 <- netmeta(p1)

summary(net1)

## Not run: 
data(Senn2013)

# Conduct common effects network meta-analysis
#
net2 <- netmeta(TE, seTE, treat1, treat2, studlab,
  data = Senn2013, sm = "MD", random = FALSE)
print(net2, ref = "plac", digits = 3)
summary(net2)

# Conduct random effects network meta-analysis
#
net3 <- netmeta(TE, seTE, treat1, treat2, studlab,
  data = Senn2013, sm = "MD", common = FALSE)
print(net3, ref = "plac", digits = 3)
summary(net3)

## End(Not run)

netmeta documentation built on June 23, 2024, 9:06 a.m.