# netleague

##### Print league table for network meta-analysis results

A league table is a square matrix showing all pairwise comparisons in a network meta-analysis. Typically, both treatment estimates and confidence intervals are shown.

##### Usage

```
netleague(x, y,
comb.fixed = x$comb.fixed, comb.random = x$comb.random,
seq = x$seq, ci = TRUE, backtransf = TRUE,
digits = gs("digits"))
```

##### Arguments

- x
An object of class

`netmeta`

(mandatory).- y
An object of class

`netmeta`

(optional).- comb.fixed
A logical indicating whether a league table for fixed effect meta-analyses should be printed.

- comb.random
A logical indicating whether a league table for random effects meta-analyses should be printed.

- 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

`backtransf=TRUE`

, results for`sm="OR"`

are printed as odds ratios rather than log odds ratios, for example.- digits
Minimal number of significant digits, see

`print.default`

.

##### Details

If argument `y`

is not provided, the league table contains the
same information in the lower and upper triangle, i.e., treatment
comparisons and confidence intervals for network meta-analysis
object `x`

.

If argument `y`

is provided, the league table contains
information on treatment comparisons from network meta-analysis
object `x`

in the lower triangle and from network meta-analysis
object `y`

in the upper triangle.

R function `netrank`

can be used to change the order of
rows and columns in the league table (see examples).

##### See Also

##### Examples

```
# NOT RUN {
# Network meta-analysis of count mortality statistics
#
data(Woods2010)
p0 <- pairwise(treatment, event = r, n = N,
studlab = author, data = Woods2010, sm = "OR")
net0 <- netmeta(p0)
cilayout(bracket = "(", separator = " - ")
oldopts <- options(width = 100)
# League table for fixed effect model
#
netleague(net0, digits = 2)
# League table for fixed effect and random effects model
#
netleague(net0, comb.random = TRUE, digits = 2)
# Change order of treatments according to treatment ranking
#
netleague(net0, comb.random = TRUE, digits = 2,
seq = netrank(net0))
#
print(netrank(net0), comb.random = TRUE)
# Use depression dataset
#
data(Linde2015)
cilayout()
#
# Define order of treatments
#
trts <- c("TCA", "SSRI", "SNRI", "NRI",
"Low-dose SARI", "NaSSa", "rMAO-A", "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,
comb.fixed = FALSE, comb.random = TRUE,
seq = trts, ref = "Placebo")
#
# (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,
comb.fixed = FALSE, comb.random = TRUE,
seq = trts, ref = "Placebo")
options(width = 200)
netleague(net1, digits = 2)
netleague(net1, digits = 2, ci = FALSE)
netleague(net2, digits = 2, ci = FALSE)
netleague(net1, net2, digits = 2, ci = FALSE)
netleague(net1, net2, seq = netrank(net1, small = "bad"), ci = FALSE)
netleague(net1, net2, seq = netrank(net2, small = "bad"), ci = FALSE)
print(netrank(net1, small = "bad"), comb.random = TRUE)
print(netrank(net2, small = "bad"), comb.random = TRUE)
options(oldopts)
# }
# NOT RUN {
# Generate a partial order of treatment rankings
#
np <- netposet(net1, net2, outcomes = outcomes, small.values = rep("bad",2))
hasse(np)
plot(np)
# }
```

*Documentation reproduced from package netmeta, version 0.9-5, License: GPL (>= 2)*