#
# 1) Smoking cessation example
#
data(smokingcessation)
# Transform data from arm-based format to contrast-based format
#
pw1 <- pairwise(list(treat1, treat2, treat3),
event = list(event1, event2, event3), n = list(n1, n2, n3),
data = smokingcessation, sm = "OR")
# Conduct random effects network meta-analysis
#
net1 <- netmeta(pw1, common = FALSE)
net1
# Draw network graphs
#
netgraph(net1, points = TRUE, cex.points = 3, cex = 1.25)
tname <- c("No intervention", "Self-help",
"Individual counselling", "Group counselling")
netgraph(net1, points = TRUE, cex.points = 3, cex = 1.25, labels = tname)
# Forest plot
#
forest(net1)
# \donttest{
#
# 2) Diabetes example
#
data(Senn2013)
# Conduct common effects network meta-analysis
#
net2 <- netmeta(TE, seTE, treat1, treat2, studlab,
data = Senn2013, sm = "MD", random = FALSE)
net2
net2$Q.decomp
# Comparison with reference group
#
print(net2, reference = "plac")
forest(net2, reference = "plac")
# Print detailed results
#
snet2 <- summary(net2)
print(snet2, digits = 3)
# Only show individual study results for multi-arm studies
#
print(snet2, digits = 3, truncate = multiarm)
# Only show first three individual study results
#
print(snet2, digits = 3, truncate = 1:3)
# Only show individual study results for Kim2007 and Willms1999
#
print(snet2, digits = 3, truncate = c("Kim2007", "Willms1999"))
# Only show individual study results for studies starting with the
# letter "W"
#
print(snet2, ref = "plac", digits = 3,
truncate = substring(studlab, 1, 1) == "W")
# Conduct random effects network meta-analysis
#
net3 <- netmeta(TE, seTE, treat1, treat2, studlab,
data = Senn2013, sm = "MD", common = FALSE,
reference = "plac")
net3
forest(net3, xlim = c(-1.5, 1), xlab = "HbA1c difference")
# Add column with P-Scores on right side of forest plot
#
forest(net3, xlim = c(-1.5, 1),
xlab = "HbA1c difference",
rightcols = c("effect", "ci", "Pscore"),
just.addcols = "right")
# Add column with P-Scores on left side of forest plot
#
forest(net3, xlim = c(-1.5, 1),
xlab = "HbA1c difference",
leftcols = c("studlab", "Pscore"),
just.addcols = "right")
# Sort forest plot by descending P-Score
#
forest(net3, xlim = c(-1.5, 1),
xlab = "HbA1c difference",
rightcols = c("effect", "ci", "Pscore"),
just.addcols = "right",
sortvar = -Pscore)
# Sort by and print number of studies with direct treatment comparisons
#
forest(net3, xlim = c(-1.5, 1),
xlab = "HbA1c difference",
leftcols = c("studlab", "k"),
leftlabs = c("Contrast\nto Placebo", "Direct\nComparisons"),
sortvar = -k,
smlab = "Random Effects Model")
# Change printing order of treatments with placebo last and use
# long treatment names
#
trts <- c("acar", "benf", "metf", "migl", "piog",
"rosi", "sita", "sulf", "vild", "plac")
#
net4 <- netmeta(TE, seTE, treat1.long, treat2.long, studlab,
data = Senn2013, sm = "MD", common = FALSE,
reference = "Placebo", seq = trts)
print(net4, digits = 2)
#
# 3) Dietary fat example
#
data(dietaryfat)
# Transform data from arm-based format to contrast-based format
# Using incidence rate ratios (sm = "IRR") as effect measure.
# Note, the argument 'sm' is not necessary as this is the default
# in R function metainc() called internally
#
pw5 <- pairwise(list(treat1, treat2, treat3),
list(d1, d2, d3), time = list(years1, years2, years3),
studlab = ID, data = dietaryfat, sm = "IRR")
pw5
# Conduct network meta-analysis
#
net5 <- netmeta(pw5)
net5
# Conduct network meta-analysis using incidence rate differences
# (sm = "IRD")
#
pw6 <- pairwise(list(treat1, treat2, treat3),
list(d1, d2, d3), time = list(years1, years2, years3),
studlab = ID, data = dietaryfat, sm = "IRD")
net6 <- netmeta(pw6)
net6
# Draw network graph
#
netgraph(net5, points = TRUE, cex.points = 3, cex = 1.25,
labels = c("Control","Diet", "Diet 2"))
# }
Run the code above in your browser using DataLab