netleague  R Documentation 
A league table is a square matrix showing all pairwise comparisons in a network metaanalysis. Typically, both treatment estimates and confidence intervals are shown.
netleague( x, y, common = x$common, random = x$random, seq = x$seq, ci = TRUE, backtransf = TRUE, direct = FALSE, digits = gs("digits"), big.mark = gs("big.mark"), text.NA = ".", bracket = gs("CIbracket"), separator = gs("CIseparator"), lower.blank = gs("CIlower.blank"), upper.blank = gs("CIupper.blank"), writexl = !missing(path), path = "leaguetable.xlsx", overwrite = FALSE, warn.deprecated = gs("warn.deprecated"), ... ) ## S3 method for class 'netleague' print( x, common = x$x$common, random = x$x$random, warn.deprecated = gs("warn.deprecated"), ... )
x 
An object of class 
y 
An object of class 
common 
A logical indicating whether a league table should be printed for the common effects network metaanalysis. 
random 
A logical indicating whether a league table should be printed for the random effects network metaanalysis. 
seq 
A character or numerical vector specifying the sequence of treatments in rows and columns of a league table. 
ci 
A logical indicating whether confidence intervals should be shown. 
backtransf 
A logical indicating whether printed results
should be back transformed. If 
direct 
A logical indicating whether league table with
network estimates (default) or estimates from direct comparisons
should be generated if argument 
digits 
Minimal number of significant digits, see

big.mark 
A character used as thousands separator. 
text.NA 
A character string to label 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. 
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.deprecated 
A logical indicating whether warnings should be printed if deprecated arguments are used. 
... 
Additional arguments (passed on to 
A league table is a square matrix showing all pairwise comparisons in a network metaanalysis (Hutton et al., 2015). Typically, both treatment estimates and confidence intervals are shown.
If argument y
is not provided, the league table contains the
network estimates from network metaanalysis object x
in the
lower triangle and the direct treatment estimates from pairwise
comparisons in the upper triangle. Note, 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
.
If argument y
is provided, the league table contains
information on treatment comparisons from network metaanalysis
object x
in the lower triangle and from network
metaanalysis object y
in the upper triangle. This is, for
example, useful to print information on efficacy and safety in the
same league table.
By default, an R object with the league tables is
generated. Alternatively, an Excel file is created if argument
writexl = TRUE
.
This implementation reports pairwise comparisons of the treatment in the row versus the treatment in the column in the lower triangle and column versus row in the upper triangle. This is a common presentation for network metaanalyses which allows to easily compare direction and magnitude of treatment effects. For example, given treatments A, B, and C, the results reported in the first row and second column as well as second row and first column are from the pairwise comparison A versus B. Note, this presentation is different from the printout of a network metaanalysis object which reports opposite pairwise comparisons in the lower and upper triangle, e.g., A versus B in the first row and second column and B versus A in the second row and first column.
If the same network metaanalysis object is used for arguments
x
and y
, reciprocal treatment estimates will be shown
in the upper triangle (see examples), e.g., the comparison B versus
A.
R function netrank
can be used to change the order of
rows and columns in the league table (see examples).
An object of class netleague
with corresponding print
function if writexl = FALSE
. The object is a list containing
the league 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, Gerta Rücker ruecker@imbi.unifreiburg.de
Hutton B, Salanti G, Caldwell DM, et al. (2015): The PRISMA Extension Statement for Reporting of Systematic Reviews Incorporating Network Metaanalyses of Health Care Interventions: Checklist and Explanations. Annals of Internal Medicine, 162, 777
netmeta
, netposet
,
netrank
# Network metaanalysis of count mortality statistics # data(Woods2010) p0 < pairwise(treatment, event = r, n = N, studlab = author, data = Woods2010, sm = "OR") net0 < netmeta(p0) oldopts < options(width = 100) # League table for common and random effects model with #  network estimates in lower triangle #  direct estimates in upper triangle # netleague(net0, digits = 2, bracket = "(", separator = "  ") # League table for common effects model # netleague(net0, random = FALSE, digits = 2) # Change order of treatments according to treatment ranking (random # effects model) # netleague(net0, common = FALSE, digits = 2, seq = netrank(net0)) # print(netrank(net0), common = FALSE) ## Not run: # Create a CSV file with league table for random effects model # league0 < netleague(net0, digits = 2, bracket = "(", separator = " to ") # write.table(league0$random, file = "league0random.csv", row.names = FALSE, col.names = FALSE, sep = ",") # # Create Excel files with league tables # (if R package writexl is available) # netleague(net0, digits = 2, bracket = "(", separator = " to ", path = tempfile(fileext = ".xlsx")) ## End(Not run) # Use depression dataset # data(Linde2015) # Define order of treatments # trts < c("TCA", "SSRI", "SNRI", "NRI", "Lowdose SARI", "NaSSa", "rMAOA", "Hypericum", "Placebo") # Outcome labels # outcomes < c("Early response", "Early remission") # (1) Early response # p1 < pairwise(treat = list(treatment1, treatment2, treatment3), event = list(resp1, resp2, resp3), n = list(n1, n2, n3), studlab = id, data = Linde2015, sm = "OR") # net1 < netmeta(p1, common = FALSE, seq = trts, ref = "Placebo", small = "bad") # (2) Early remission # p2 < pairwise(treat = list(treatment1, treatment2, treatment3), event = list(remi1, remi2, remi3), n = list(n1, n2, n3), studlab = id, data = Linde2015, sm = "OR") # net2 < netmeta(p2, common = FALSE, seq = trts, ref = "Placebo", small = "bad") options(width = 200) netleague(net1, digits = 2) netleague(net1, digits = 2, ci = FALSE) netleague(net2, digits = 2, ci = FALSE) # League table for two outcomes with #  network estimates of first outcome in lower triangle #  network estimates of second outcome in upper triangle # netleague(net1, net2, digits = 2, ci = FALSE) netleague(net1, net2, seq = netrank(net1), ci = FALSE) netleague(net1, net2, seq = netrank(net2), ci = FALSE) print(netrank(net1)) print(netrank(net2)) # Report results for network metaanalysis twice # netleague(net1, net1, seq = netrank(net1), ci = FALSE, backtransf = FALSE) netleague(net1, net1, seq = netrank(net1), ci = FALSE, backtransf = FALSE, direct = TRUE) options(oldopts) ## Not run: # Generate a partial order of treatment rankings # np < netposet(net1, net2, outcomes = outcomes) # Requires R package 'hasse' # hasse(np) plot(np) ## End(Not run)
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