MBNMAdose (version 0.4.3)

mbnma.nodesplit: 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 nodesplit
plot(x, plot.type = "forest", ...)

Value

Plots the desired graph if plot.type="forest" and plots and returns an object of class(c("gg", "ggplot")) if plot.type="density".

Arguments

network

An object of class mbnma.network.

fun

An object of class("dosefun") that specifies a functional form to be assigned to the dose-response. See Details.

method

Can take either "common" or "random" to indicate whether relative effects should be modelled with between-study heterogeneity or not (see details).

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 network) or numeric (corresponding to treatment codes within the network - note that these may change if drop.discon = TRUE).

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 ggplot2::ggplot() or forestplot::forestplot()

x

An object of class("nodesplit")

plot.type

A character string that can take the value of "forest" to plot forest plots or "density" to plot posterior density plots.

Functions

• plot(nodesplit): Plot outputs from treatment-level nodesplit MBNMA models

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.

Examples

# \donttest{
# 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
# }