Split direct and indirect evidence in network meta-analysis
Methods to split network estimates into the contribution of direct and indirect evidence and to test for local inconsistency in network meta-analysis.
netsplit(
x,
method,
upper = TRUE,
reference.group = x$reference.group,
baseline.reference = x$baseline.reference,
sep.trts = x$sep.trts,
quote.trts = "",
tol.direct = 5e-04,
comb.fixed = x$comb.fixed,
comb.random = x$comb.random,
backtransf = x$backtransf,
warn = FALSE
)
## S3 method for class 'netsplit'
print(
x,
comb.fixed = x$comb.fixed,
comb.random = x$comb.random,
show = "all",
overall = TRUE,
ci = FALSE,
test = show %in% c("all", "with.direct", "both"),
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,
...
)x |
An object of class |
method |
A character string indicating which method to split
direct and indirect evidence is to be used. Either
|
upper |
A logical indicating whether treatment comparisons
should be selected from the lower or upper triangle of the
treatment effect matrices (see list elements |
reference.group |
Reference treatment. |
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 |
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. |
comb.fixed |
A logical indicating whether results for the fixed effects (common effects) network meta-analysis should be printed. |
comb.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 |
warn |
A logical indicating whether warnings should be printed. |
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. |
digits |
Minimal number of significant digits, see
|
digits.stat |
Minimal number of significant digits for z-value
of test of agreement between direct and indirect evidence, see
|
digits.pval |
Minimal number of significant digits for p-value
of test of agreement between direct and indirect evidence, see
|
digits.prop |
Minimal number of significant digits for direct
evidence proportions, see |
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. |
... |
Additional arguments (ignored at the moment) |
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. The direct evidence proportion as described in König et al. (2013) is used in the calculation of the indirect evidence;
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 τ^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 |
An object of class netsplit with corresponding print
and forest functions. 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 / random effects model). |
fixed, random |
Results of network meta-analysis (fixed / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p. |
direct.fixed, direct.random |
Network meta-analysis results based on direct evidence (fixed / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p. |
indirect.fixed, indirect.random |
Network meta-analysis results based on indirect evidence (fixed / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p. |
compare.fixed, compare.random |
Comparison of direct and indirect evidence in network meta-analysis (fixed / 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.comb |
The level used to calculate confidence intervals for pooled estimates. |
version |
Version of R package netmeta used to create object. |
Guido Schwarzer sc@imbi.uni-freiburg.de, Gerta Rücker ruecker@imbi.uni-freiburg.de, Orestis Efthimiou oremiou@gmail.com
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, 1–21, https://doi.org/10.1002/sim.8158
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
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.fixed = FALSE)
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)
## Not run:
data(Dong2013)
p3 <- pairwise(treatment, death, randomized, studlab = id,
data = Dong2013, sm = "OR")
net3 <- netmetabin(p3)
netsplit(net3)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.