netmeta (version 0.9-5)

netsplit: Split direct and indirect evidence in network meta-analysis

Description

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)

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.

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).

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

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)
# }