Combine network meta-analysis objects
This function can be used to combine network meta-analysis objects which is especially useful to generate a forest plot with results of several network meta-analyses.
netbind( ..., name, comb.fixed, comb.random, col.study = "black", col.inside = "white", col.square = "gray", col.square.lines = col.square, backtransf, reference.group, baseline.reference )
... |
Any number of meta-analysis objects or a single list with network meta-analyses. |
name |
An optional character vector providing descriptive names for the network meta-analysis objects. |
comb.fixed |
A logical indicating whether results for the fixed effects (common effects) model should be reported. |
comb.random |
A logical indicating whether results for the random effects model should be reported. |
col.study |
The colour for network estimates and confidence limits. |
col.inside |
The colour for network estimates and confidence limits if confidence limits are completely within squares. |
col.square |
The colour for squares. |
col.square.lines |
The colour for the outer lines of squares. |
backtransf |
A logical indicating whether results should be
back transformed. If |
reference.group |
Reference treatment. |
baseline.reference |
A logical indicating whether results
should be expressed as comparisons of other treatments versus the
reference treatment (default) or vice versa. This argument is
only considered if |
An object of class "netbind"
with corresponding
forest
function. The object is a list containing the
following components:
fixed |
A data frame with results for the fixed effects model. |
random |
A data frame with results for the random effects model. |
sm |
Summary measure used in network meta-analyses. |
level.comb |
Level for confidence intervals. |
comb.fixed, comb.random, backtransf |
As defined above. |
reference.group, baseline.reference |
As defined above. |
Guido Schwarzer sc@imbi.uni-freiburg.de
data(Linde2016) # Only consider studies including Face-to-face PST (to reduce # runtime of example) # face <- subset(Linde2016, id %in% c(16, 24, 49, 118)) # Standard random effects NMA model (with placebo as reference # treatment) # net1 <- netmeta(lnOR, selnOR, treat1, treat2, id, data = face, reference.group = "placebo", sm = "OR", comb.fixed = FALSE) # Additive CNMA model with placebo as inactive component and # reference # nc1 <- netcomb(net1, inactive = "placebo") # Combine results of standard NMA and CNMA # nb1 <- netbind(nc1, net1, name = c("Additive CNMA", "Standard NMA"), col.study = c("red", "black"), col.square = c("red", "black")) forest(nb1, col.by = "black", addrow.subgroups = FALSE, fontsize = 10, spacing = 0.7, squaresize = 0.9, label.left = "Favours Placebo", label.right = "Favours other")
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