mbnma.nodesplit {MBNMAdose} | R Documentation |
Node-splitting model for testing consistency at the treatment level using MBNMA
Description
Splits contributions for a given set of treatment comparisons into direct and indirect evidence. A discrepancy between the two suggests that the consistency assumption required for NMA and MBNMA may violated.
Usage
mbnma.nodesplit(
network,
fun = dpoly(degree = 1),
method = "common",
comparisons = NULL,
incldr = TRUE,
...
)
## S3 method for class 'nodesplit'
plot(x, plot.type = "forest", ...)
Arguments
network |
An object of class |
fun |
An object of |
method |
Can take either |
comparisons |
A matrix specifying the comparisons to be split (one row per comparison).
The matrix must have two columns indicating each treatment for each comparison. Values can
either be character (corresponding to the treatment names given in |
incldr |
A boolean object indicating whether or not to allow for indirect evidence contributions via the dose-response relationship. This can be used when node-splitting in dose-response MBNMA to allow for a greater number of potential loops in which to check for consistency. |
... |
Arguments to be sent to |
x |
An object of |
plot.type |
A character string that can take the value of |
Details
The S3 method plot()
on an nodesplit
object generates either
forest plots of posterior medians and 95\% credible intervals, or density plots
of posterior densities for direct and indirect evidence.
Value
Plots the desired graph if plot.type="forest"
and plots and returns an object
of class(c("gg", "ggplot"))
if plot.type="density"
.
Functions
-
plot(nodesplit)
: Plot outputs from treatment-level nodesplit MBNMA models
Examples
# Using the triptans data
network <- mbnma.network(triptans)
split <- mbnma.nodesplit(network, fun=demax(), likelihood = "binomial", link="logit",
method="common")
#### To perform nodesplit on selected comparisons ####
# Check for closed loops of treatments with independent evidence sources
# Including indirect evidence via the dose-response relationship
loops <- inconsistency.loops(network$data.ab, incldr=TRUE)
# This...
single.split <- mbnma.nodesplit(network, fun=dexp(), likelihood = "binomial", link="logit",
method="random", comparisons=rbind(c("sumatriptan_1", "almotriptan_1")))
#...is the same as...
single.split <- mbnma.nodesplit(network, fun=dexp(), likelihood = "binomial", link="logit",
method="random", comparisons=rbind(c(6, 12)))
# Plot results
plot(split, plot.type="density") # Plot density plots of posterior densities
plot(split, txt_gp=forestplot::fpTxtGp(cex=0.5)) # Plot forest plots (with smaller label size)
# Print and summarise results
print(split)
summary(split) # Generate a data frame of summary results