netpairwise {netmeta}

Conduct pairwise meta-analyses for all comparisons with direct evidence in a network meta-analysis

Description

Conduct pairwise meta-analyses for all comparisons with direct evidence in a network meta-analysis.

Usage

netpairwise(
  x,
  separate = FALSE,
  common = x$common,
  random = x$random,
  level = x$level,
  level.ma = x$level.ma,
  prediction = x$prediction,
  level.predict = x$level.predict,
  reference.group = if (missing(order)) x$reference.group else "",
  baseline.reference = x$baseline.reference,
  method.tau = x$method.tau,
  order = NULL,
  sep.trts = x$sep.trts,
  nchar.trts = x$nchar.trts,
  backtransf = x$backtransf,
  warn.deprecated = gs("warn.deprecated"),
  ...
)

## S3 method for class 'netpairwise'
print(x, ...)

## S3 method for class 'netpairwise'
summary(object, ...)

## S3 method for class 'summary.netpairwise'
print(x, ...)

## S3 method for class 'netpairwise'
forest(x, ...)

## S3 method for class 'netpairwise'
plot(x, ...)

## S3 method for class 'netpairwise'
funnel(x, k.min = 3, ...)

## S3 method for class 'netpairwise'
radial(x, k.min = 3, ...)

## S3 method for class 'netpairwise'
baujat(x, k.min = 3, ...)

## S3 method for class 'netpairwise'
metabias(x, k.min = 10, ...)

## S3 method for class 'metabias.netpairwise'
print(x, ...)

## S3 method for class 'netpairwise'
trimfill(x, k.min = 3, ...)

## S3 method for class 'trimfill.netpairwise'
print(x, ...)

## S3 method for class 'netpairwise'
metainf(x, k.min = 2, ...)

## S3 method for class 'metainf.netpairwise'
print(x, ...)

## S3 method for class 'netpairwise'
metacum(x, k.min = 2, ...)

## S3 method for class 'metacum.netpairwise'
print(x, ...)

## S3 method for class 'netpairwise'
metareg(x, ..., k.min = 2)

## S3 method for class 'metareg.netpairwise'
print(x, ...)

Arguments

x

An object of class netmeta or netpairwise.

separate

A logical indicating whether results for pairwise comparisons should be printed as separate meta-analyses or as subgroups which is more concise.

common

A logical indicating whether a common effects network meta-analysis should be conducted.

random

A logical indicating whether a random effects network meta-analysis should be conducted.

level

The level used to calculate confidence intervals for individual comparisons.

level.ma

The level used to calculate confidence intervals for pooled estimates.

prediction

A logical indicating whether prediction intervals should be printed.

level.predict

The level used to calculate prediction intervals for a new study.

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.

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.

order

An optional character or numerical vector specifying the order of treatments.

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 (see Details).

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.

warn.deprecated

A logical indicating whether warnings should be printed if deprecated arguments are used.

...

Additional arguments (passed on to metagen or print functions and to catch deprecated arguments).

object

An object of class netpairwise.

k.min

Minimum number of studies in pairwise comparison to show funnel plot, radial plot or conduct test for funnel plot asymmetry.

Details

Conduct pairwise meta-analyses for all comparisons with direct evidence in a network meta-analysis. In contrast to netmeta and netsplit, unadjusted standard errors are used in the calculations and the between-study heterogeneity variance is allowed to differ between comparisons.

The R function metagen is called internally.

Value

Either a single metagen object with pairwise comparisons as subgroups or a list with metagen objects for each direct pairwise comparison.

Note

This function must not be confused with pairwise which can be used as a pre-processing step to convert data from arm-based to contrast-based format by calculating all pairwise comparisons within a study.

Author(s)

Guido Schwarzer [e-mail]

See Also

netmeta, netsplit, pairwise

Examples

oldsets <- settings.meta(digits = 2, digits.tau2 = 2, digits.tau = 2)

data(smokingcessation)

# Transform data from arm-based format to contrast-based format
#
p1 <- pairwise(list(treat1, treat2, treat3),
  event = list(event1, event2, event3), n = list(n1, n2, n3),
  data = smokingcessation, sm = "OR")

# Conduct random effects network meta-analysis
#
net1 <- netmeta(p1, common = FALSE)

# Calculate and print concise results for all pairwise
# meta-analyses
#
np1 <- netpairwise(net1)
np1
print(np1, details.method = FALSE)

## Not run: 
data(Senn2013)

# Random effects model
#
net2 <- netmeta(TE, seTE, treat1.long, treat2.long, studlab,
  data = Senn2013, sm = "MD", common = FALSE, reference = "plac")

# Calculate and print concise results for all pairwise
# meta-analyses
#
np2 <- netpairwise(net2)
np2
print(np2, details.method = FALSE)

forest(np2)

# Print detailed information for each pairwise comparison
#
np3 <- netpairwise(net2, separate = TRUE)
forest(np3)
funnel(np3)
radial(np3)
funnel(np3, k.min = 1)

## End(Not run)

settings.meta(oldsets)