nettable  R Documentation 
Construct a table with network, direct and indirect estimates from one or more network metaanalyses.
nettable( ..., name = NULL, method = NULL, order = NULL, common, random, upper = TRUE, reference.group = NULL, baseline.reference = NULL, backtransf = 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 = 5e04, writexl = !missing(path), path = "nettable.xlsx", overwrite = FALSE, warn = FALSE, verbose = FALSE ) ## S3 method for class 'nettable' print(x, common = x$x$common, random = x$x$random, ...)
... 
Any number of network metaanalysis objects or a single list with network metaanalyses. 
name 
An optional character vector providing descriptive names for network metaanalysis objects. 
method 
A character string indicating which method to split
direct and indirect evidence is to be used. Either

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 metaanalysis should be printed. 
random 
A logical indicating whether table for the random effects network metaanalysis 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 
reference.group 
Reference treatment. Ignored if argument

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 
backtransf 
A logical indicating whether printed results
should be back transformed. For example, if 
digits 
Minimal number of significant digits, see

digits.I2 
Minimal number of significant digits for Isquared
statistic, see 
digits.pval 
Minimal number of significant digits for pvalue
of test of agreement between direct and indirect evidence, see

scientific.pval 
A logical specifying whether pvalues should be printed in scientific notation, e.g., 1.2345e01 instead of 0.12345. 
zero.pval 
A logical specifying whether pvalues should be printed with a leading zero. 
JAMA.pval 
A logical specifying whether pvalues 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 
Construct a table with network, direct and indirect estimates from one or more network metaanalyses. The table looks very similar to the statistical part of a GRADE table for a network metaanalysis (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 backcalculation 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 backcalculation method, indirect treatment estimates
are already calculated in netmeta
and this function
combines and prints these estimates in a userfriendly
way. Furthermore, this method is not available for the
MantelHaenszel and noncentral hypergeometric distribution
approach implemented in netmetabin
.
For the randomeffects model, the direct treatment estimates are
based on the common betweenstudy variance τ^2 from the
network metaanalysis, i.e. the square of list element
x$tau
.
The SIDDE approach can be computeintensive in large
networks. Crude information on the computation progress is printed
for SIDDE if argument verbose
is TRUE
.
An object of class nettable
with corresponding print
function if argument writexl = FALSE
. The object is a list
containing the network tables in list elements 'common' and
'random'. An Excel file is created if writexl = TRUE
. In
this case, NULL
is returned in R.
Guido Schwarzer sc@imbi.unifreiburg.de
Efthimiou O, Rücker G, Schwarzer G, Higgins J, Egger M, Salanti G (2019): A MantelHaenszel model for network metaanalysis of rare events. Statistics in Medicine, 1–21, https://doi.org/10.1002/sim.8158
König J, Krahn U, Binder H (2013): Visualizing the flow of evidence in network metaanalysis and characterizing mixed treatment comparisons. Statistics in Medicine, 32, 5414–29
Puhan MA, Schünemann HJ, Murad MH, et al. (2014): A GRADE working group approach for rating the quality of treatment effect estimates from network metaanalysis. British Medical Journal, 349, g5630
netsplit
, netmeta
,
netmetabin
, netmeasures
data(Woods2010) # p1 < pairwise(treatment, event = r, n = N, studlab = author, data = Woods2010, sm = "OR") # net1 < netmeta(p1) # nt1 < nettable(net1, digits = 2) nt1 print(nt1, common = FALSE) print(nt1, random = FALSE) ## Not run: # Create a CSV file with network table from random effects model # table1 < nettable(net1, digits = 2, bracket = "(", separator = " to ") # write.table(table1$random, file = "table1random.csv", row.names = FALSE, col.names = TRUE, sep = ",") # # Create Excel files with network tables # (if R package writexl is available) # nettable(net1, digits = 2, bracket = "(", separator = " to ", path = tempfile(fileext = ".xlsx")) ## End(Not run)
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