netsplit

0th

Percentile

Split direct and indirect evidence in network meta-analysis

Split contribution of direct and indirect evidence in network meta-analysis.

Usage
netsplit(x)

# S3 method for netsplit print(x, comb.fixed = x$comb.fixed, comb.random = x$comb.random, showall = TRUE, overall = TRUE, ci = FALSE, test = TRUE, digits = gs("digits"), digits.zval = gs("digits.zval"), digits.pval = gs("digits.pval"), text.NA = ".", backtransf = TRUE, ...)

Arguments
x

An object of class netmeta or netsplit.

comb.fixed

A logical indicating whether results for fixed effect model should be printed.

comb.random

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

showall

A logical indicating whether all comparisons (default) or only comparisons contributing both direct and indirect evidence should be printed.

overall

A logical indicating whether estimates from network meta-analyis should be printed in addition to direct and indirect estimates.

ci

A logical indicating whether confidence intervals should be printed in addition to treatment estimates.

test

A logical indicating whether results of a test comparing direct and indirect estimates should be printed.

digits

Minimal number of significant digits, see print.default.

digits.zval

Minimal number of significant digits for z-value of test of agreement between direct and indirect evidence, see print.default.

digits.pval

Minimal number of significant digits for p-value of test of agreement between direct and indirect evidence, see print.default.

backtransf

A logical indicating whether printed results should be back transformed. For example, if backtransf=TRUE, results for sm="OR" are printed as odds ratios rather than log odds ratios.

text.NA

A character string specifying text printed for missing values.

...

Additional arguments (ignored at the moment)

Details

Direct and indirect treatment estimates are calculated in netmeta. This function combines and prints these estimates in a user-friendly way.

A comparison of direct and indirect treatment estimates can serve as check for consistency of network meta-analysis (Dias et al., 2010).

Value

An object of class netsplit with corresponding print function. The object is a list containing the following components:

comb.fixed, comb.random

As defined above.

comparison

A vector with treatment comparisons.

prop.fixed, prop.random

A vector with direct evidence proportions (fixed effect / random effects model).

fixed, random

Results of network meta-analysis (fixed effect / random effects model), i.e., list with vectors TE, seTE, lower, upper, z, and p.

direct.fixed, direct.random

Network meta-analysis results based on direct evidence (fixed effect / random effects model), i.e., list with vectors TE, seTE, lower, upper, z, and p.

indirect.fixed, indirect.random

Network meta-analysis results based on indirect evidence (fixed effect / random effects model), i.e., list with vectors TE, seTE, lower, upper, z, and p.

compare.fixed, compare.random

Comparison of direct and indirect evidence in network meta-analysis (fixed effect / random effects model), i.e., list with vectors TE, seTE, lower, upper, z, and p.

sm

A character string indicating underlying summary measure

level.comb

The level used to calculate confidence intervals for pooled estimates.

version

Version of R package netmeta used to create object.

References

Dias S, Welton NJ, Caldwell DM, Ades AE (2010). Checking consistency in mixed treatment comparison meta-analysis. Statistics in Medicine, 29, 932--44.

Puhan MA, Sch<U+00FC>nemann HJ, Murad MH, et al. (2014). A GRADE working group approach for rating the quality of treatment effect estimates from network meta-analysis. British Medical Journal, 349, g5630

See Also

netmeta, netmeasures

Aliases
  • netsplit
  • print.netsplit
Examples
# NOT RUN {
data(Woods2010)
#
p1 <- pairwise(treatment, event = r, n = N,
               studlab = author, data = Woods2010, sm = "OR")
#
net1 <- netmeta(p1)
#
print(netsplit(net1), digits = 2)
print(netsplit(net1), digits = 2,
      backtransf = FALSE, comb.random = TRUE)

data(Senn2013)
#
net2 <- netmeta(TE, seTE, treat1, treat2, studlab, data = Senn2013,
                comb.random = TRUE)
#
print(netsplit(net2), digits = 2)
# Layout of Puhan et al. (2014), Table 1
print(netsplit(net2), digits = 2, ci = TRUE, test = FALSE)
# }
Documentation reproduced from package netmeta, version 0.9-5, License: GPL (>= 2)

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