# \donttest{
# Only consider studies including Face-to-face PST (to reduce
# runtime of example)
#
face <- subset(Linde2016, id %in% c(16, 24, 49, 118))
# Use pairwise() to transform data to comparison-based format
#
pw <- pairwise(treat = int,
event = resp, n = n, studlab = paste(author, year),
data = face, reference = "plac", sm = "OR")
# Conduct random effects network meta-analysis
#
nma <- netmeta(pw, common = FALSE)
# Additive CNMA model with placebo as inactive component and
# reference
#
cnma <- netcomb(nma, inactive = "placebo")
# Combine results of standard NMA and CNMA
#
nb <- netbind(cnma, nma,
name = c("Additive CNMA", "Standard NMA"),
col.study = c("red", "black"), col.square = c("red", "black"))
#
nb
#
forest(nb,
col.subgroup = "black", addrow.subgroups = FALSE,
fontsize = 10, spacing = 0.7, squaresize = 0.9,
label.left = "Favours Placebo",
label.right = "Favours other")
# Also show results for common effects model
#
print(nb, common = TRUE)
# }
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