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

netsplit {netmeta}

R Documentation

Split direct and indirect evidence in network meta-analysis

Description

Methods to split network estimates into the contribution of direct and indirect evidence and to test for local inconsistency in network meta-analysis.

Usage

netsplit(
  x,
  method,
  upper = TRUE,
  reference.group = x$reference.group,
  baseline.reference = x$baseline.reference,
  order = NULL,
  sep.trts = x$sep.trts,
  quote.trts = "",
  tol.direct = 5e-04,
  common = x$common,
  random = x$random,
  backtransf = x$backtransf,
  warn = FALSE,
  warn.deprecated = gs("warn.deprecated"),
  verbose = FALSE,
  ...
)

## S3 method for class 'netsplit'
print(
  x,
  common = x$x$common,
  random = x$x$random,
  show = "all",
  overall = TRUE,
  ci = FALSE,
  test = show %in% c("all", "with.direct", "both"),
  only.reference = FALSE,
  sortvar = NULL,
  subset = NULL,
  nchar.trts = x$nchar.trts,
  digits = gs("digits"),
  digits.stat = gs("digits.stat"),
  digits.pval = gs("digits.pval"),
  digits.prop = max(gs("digits.pval") - 2, 2),
  text.NA = ".",
  backtransf = x$backtransf,
  scientific.pval = gs("scientific.pval"),
  big.mark = gs("big.mark"),
  legend = TRUE,
  indent = TRUE,
  warn.deprecated = gs("warn.deprecated"),
  ...
)

Arguments

`x`	An object of class `netmeta` or `netsplit`.
`method`	A character string indicating which method to split direct and indirect evidence is to be used. Either `"Back-calculation"` or `"SIDDE"`, can be abbreviated. See Details.
`upper`	A logical indicating whether treatment comparisons should be selected from the lower or upper triangle of the treatment effect matrices (see list elements `TE.common` and `TE.random` in the `netmeta` object). Ignored if argument `order` is provided.
`reference.group`	Reference treatment. Ignored if argument `order` is provided.
`baseline.reference`	A logical indicating whether results should be expressed as comparisons of other treatments versus the reference treatment or vice versa. This argument is only considered if `reference.group` is not equal to `""` and argument`order` is not provided.
`order`	A optional character or numerical vector specifying the order of treatments in comparisons.
`sep.trts`	A character string used in comparison names as separator between treatment labels, e.g., " vs ".
`quote.trts`	A character used to print around treatment labels.
`tol.direct`	A numeric defining the maximum deviation of the direct evidence proportion from 0 or 1 to classify a comparison as providing only indirect or direct evidence, respectively.
`common`	A logical indicating whether results for the common effects network meta-analysis should be printed.
`random`	A logical indicating whether results for the random effects network meta-analysis should be printed.
`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.
`warn`	A logical indicating whether warnings should be printed.
`warn.deprecated`	A logical indicating whether warnings should be printed if deprecated arguments are used.
`verbose`	A logical indicating whether progress information should be printed.
`...`	Additional arguments.
`show`	A character string indicating which comparisons should be printed (see Details).
`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.
`only.reference`	A logical indicating whether only comparisons with the reference group should be printed.
`sortvar`	An optional vector used to sort comparisons (must be of same length as the total number of comparisons).
`subset`	An optional logical vector specifying a subset of comparisons to print (must be of same length as the total number of comparisons) .
`nchar.trts`	A numeric defining the minimum number of characters used to create unique treatment names.
`digits`	Minimal number of significant digits, see `print.default`.
`digits.stat`	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`.
`digits.prop`	Minimal number of significant digits for direct evidence proportions, see `print.default`.
`text.NA`	A character string specifying text printed for missing values.
`scientific.pval`	A logical specifying whether p-values should be printed in scientific notation, e.g., 1.2345e-01 instead of 0.12345.
`big.mark`	A character used as thousands separator.
`legend`	A logical indicating whether a legend should be printed.
`indent`	A logical indicating whether items in the legend should be indented.

Details

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

This function provides two methods to derive indirect estimates:

Separate Indirect from Direct Evidence (SIDE) using a back-calculation method (method = "Back-calculation") based on the direct evidence proportion to calculate the indirect evidence (König et al., 2013);
Separate Indirect from Direct Design Evidence (SIDDE) as described in Efthimiou et al. (2019).

Note, for the back-calculation method, indirect treatment estimates are already calculated in netmeta and this function combines and prints these estimates in a user-friendly way. Furthermore, this method is not available for the Mantel-Haenszel and non-central hypergeometric distribution approach implemented in netmetabin.

For the random-effects model, the direct treatment estimates are based on the common between-study variance \tau^2 from the network meta-analysis, i.e. the square of list element x$tau.

Argument show determines which comparisons are printed:

“all”	All comparisons
“both”	Only comparisons contributing both direct and indirect evidence
“with.direct”	Comparisons providing direct evidence
“direct.only”	Comparisons providing only direct evidence
“indirect.only”	Comparisons providing only indirect evidence

The node-splitting method and SIDDE can be compute-intensive in large networks. Crude information on the computation progress is printed if argument verbose = TRUE. In addition, computation times are printed if R package tictoc is installed.

Value

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

`common`, `random`	As defined above.
`comparison`	A vector with treatment comparisons.
`prop.common`, `prop.random`	A vector with direct evidence proportions (common / random effects model).
`common`, `random`	Results of network meta-analysis (common / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p.
`direct.common`, `direct.random`	Network meta-analysis results based on direct evidence (common / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p.
`indirect.common`, `indirect.random`	Network meta-analysis results based on indirect evidence (common / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p.
`compare.common`, `compare.random`	Comparison of direct and indirect evidence in network meta-analysis (common / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p.
`sm`	A character string indicating underlying summary measure
`level.ma`	The level used to calculate confidence intervals for pooled estimates.
`tictoc`	Computation times for node-splitting method or SIDDE (if R package tictoc is installed).
`version`	Version of R package netmeta used to create object.

Author(s)

Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de, Gerta Rücker gerta.ruecker@uniklinik-freiburg.de, Orestis Efthimiou oremiou@gmail.com

References

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

Efthimiou O, Rücker G, Schwarzer G, Higgins J, Egger M, Salanti G (2019): A Mantel-Haenszel model for network meta-analysis of rare events. Statistics in Medicine, 38, 2992–3012

König J, Krahn U, Binder H (2013): Visualizing the flow of evidence in network meta-analysis and characterizing mixed treatment comparisons. Statistics in Medicine, 32, 5414–29

Puhan MA, Schünemann 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

Examples

data(Woods2010)
#
p1 <- pairwise(treatment, event = r, n = N,
  studlab = author, data = Woods2010, sm = "OR")
#
net1 <- netmeta(p1)
#
print(netsplit(net1), digits = 2)

## Not run: 
print(netsplit(net1), digits = 2,
  backtransf = FALSE, common = FALSE)

# Sort by increasing number of studies in direct comparisons
print(netsplit(net1), digits = 2, sortvar = k)
# Sort by decreasing number of studies in direct comparisons
print(netsplit(net1), digits = 2, sortvar = -k)

# Sort by increasing evidence proportion under common effects model
print(netsplit(net1), digits = 2, sortvar = prop.common)
# Sort by decreasing evidence proportion under common effects model
print(netsplit(net1), digits = 2, sortvar = -prop.common)

# Sort by decreasing evidence proportion under common effects model
# and number of studies
print(netsplit(net1), digits = 2, sortvar = cbind(-prop.common, -k))

data(Senn2013)
#
net2 <- netmeta(TE, seTE, treat1.long, treat2.long,
  studlab, data = Senn2013)
#
print(netsplit(net2), digits = 2)
# Layout of Puhan et al. (2014), Table 1
print(netsplit(net2), digits = 2, ci = TRUE, test = FALSE)

data(Dong2013)
p3 <- pairwise(treatment, death, randomized, studlab = id,
  data = Dong2013, sm = "OR")
net3 <- netmetabin(p3)
netsplit(net3)

## End(Not run)

[Package netmeta version 2.9-0 Index]