nettable: Table with network meta-analysis results

Description

Construct a table with network, direct and indirect estimates from one or more network meta-analyses.

Usage

nettable(
  ...,
  name = NULL,
  method = NULL,
  order = NULL,
  common,
  random,
  upper = TRUE,
  reference.group = NULL,
  baseline.reference = NULL,
  backtransf = NULL,
  nchar.trts = if (writexl) 666 else NULL,
  digits = gs("digits"),
  digits.I2 = gs("digits.I2"),
  digits.pval = gs("digits.pval"),
  scientific.pval = gs("scientific.pval"),
  zero.pval = gs("zero.pval"),
  JAMA.pval = gs("JAMA.pval"),
  big.mark = gs("big.mark"),
  text.NA = ".",
  bracket = gs("CIbracket"),
  separator = gs("CIseparator"),
  lower.blank = gs("CIlower.blank"),
  upper.blank = gs("CIupper.blank"),
  tol.direct = 5e-04,
  writexl = !missing(path),
  path = "nettable.xlsx",
  overwrite = FALSE,
  warn = FALSE,
  verbose = FALSE
)
# S3 method for nettable
print(x, common = x$x$common, random = x$x$random, legend = gs("legend"), ...)

Value

An object of class nettable with corresponding print

function if argument writexl = FALSE. The object is a list containing the network tables in list elements 'common' and 'random'. An Excel file is created if writexl = TRUE. In this case, NULL is returned in R.

Arguments

...: Any number of network meta-analysis objects or a single list with network meta-analyses.
name: An optional character vector providing descriptive names for network meta-analysis objects.
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.
order: A optional character or numerical vector specifying the order of treatments in comparisons.
common: A logical indicating whether table for the common effects network meta-analysis should be printed.
random: A logical indicating whether table for the random effects network meta-analysis should be printed.
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 argumentorder is not provided.
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.
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.I2: Minimal number of significant digits for I-squared statistic, 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.
scientific.pval: A logical specifying whether p-values should be printed in scientific notation, e.g., 1.2345e-01 instead of 0.12345.
zero.pval: A logical specifying whether p-values should be printed with a leading zero.
JAMA.pval: A logical specifying whether p-values for test of overall effect should be printed according to JAMA reporting standards.
big.mark: A character used as thousands separator.
text.NA: A character string specifying text printed for missing values.
bracket: A character with bracket symbol to print lower confidence interval: "[", "(", "{", "".
separator: A character string with information on separator between lower and upper confidence interval.
lower.blank: A logical indicating whether blanks between left bracket and lower confidence limit should be printed.
upper.blank: A logical indicating whether blanks between separator and upper confidence limit should be printed.
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.
writexl: A logical indicating whether an Excel file should be created (R package writexl must be available).
path: A character string specifying the filename of the Excel file.
overwrite: A logical indicating whether an existing Excel file should be overwritten.
warn: A logical indicating whether warnings should be printed.
verbose: A logical indicating whether progress information should be printed.
x: An object of class nettable.
legend: A logical indicating whether a legend should be printed for abbreviated treatment names.

Author

Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de

Details

Construct a table with network, direct and indirect estimates from one or more network meta-analyses. The table looks very similar to the statistical part of a GRADE table for a network meta-analysis (Puhan et al., 2014).

By default, an R object with the network tables is generated. Alternatively, an Excel file is created if argument writexl = TRUE.

Two methods to derive indirect estimates are available:

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.

The SIDDE approach can be compute-intensive in large networks. Crude information on the computation progress is printed for SIDDE if argument verbose is TRUE.

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

Run this code

pw1 <- pairwise(treatment, event = r, n = N,
  studlab = author, data = dat.woods2010, sm = "OR")
#
net1 <- netmeta(pw1)
#
nt1 <- nettable(net1, digits = 2)
nt1

# \donttest{
print(nt1, common = FALSE)
print(nt1, random = FALSE)

# Create a CSV file with network table from random effects model
#
table1 <- nettable(net1, digits = 2, bracket = "(", separator = " to ")
#
write.table(table1$random, file = "table1-random.csv",
  row.names = FALSE, col.names = TRUE, sep = ",")
#
# Create Excel files with network tables
# (if R package writexl is available)
#
if (requireNamespace("writexl", quietly = TRUE))
  nettable(net1, digits = 2, bracket = "(", separator = " to ",
           path = tempfile(fileext = ".xlsx"))
# }

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