netmeta (version 0.9-5)

netleague: Print league table for network meta-analysis results

Description

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

netmeta, netposet, netrank

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)
# }