netconnection.crossnma: Get information on network connectivity (number of subnetworks, distance matrix)

Description

To determine the network structure and to test whether a given network is fully connected. The function calculates the number of subnetworks (connectivity components; value of 1 corresponds to a fully connected network) and the distance matrix (in block-diagonal form in the case of subnetworks). If some treatments are combinations of

Usage

# S3 method for crossnma
netconnection(data, ...)

Value

An object of class netconnection with corresponding print function. The object is a list containing the following components:

treat1, treat2, studlab, title, warn, nchar.trts: As defined above.
k: Total number of studies.
m: Total number of pairwise comparisons.
n: Total number of treatments.
n.subnets: Number of subnetworks; equal to 1 for a fully connected network.
D.matrix: Distance matrix.
A.matrix: Adjacency matrix.
L.matrix: Laplace matrix.
call: Function call.
version: Version of R package netmeta used to create object.

Arguments

data: An object produced by crossnma.
...: ... Additional arguments (passed on to netconnection).

Author

Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de

Examples

Run this code

if (FALSE) {
# We conduct a network meta-analysis assuming a random-effects
# model.
# The data comes from randomized-controlled trials and
# non-randomized studies (combined naively)
head(ipddata) # participant-level data
stddata # study-level data

# Create a JAGS model
mod <- crossnma.model(treat, id, relapse, n, design,
  prt.data = ipddata, std.data = stddata,
  reference = "A", trt.effect = "random", method.bias = "naive")

# Fit JAGS model
set.seed(1909)
fit <- crossnma(mod)

# Check network connectivity
netconnection(fit)
}