View source: R/nma_data-class.R
get_nodesplits | R Documentation |
Determine whether two treatments in a network are connected by direct and/or indirect evidence, and generate a list of comparisons with both direct and indirect evidence (i.e. potential inconsistency) for node-splitting.
get_nodesplits(network, include_consistency = FALSE)
has_direct(network, trt1, trt2)
has_indirect(network, trt1, trt2)
network |
An |
include_consistency |
Logical, whether to include a row of |
trt1 , trt2 |
Treatments, each as a single integer, string, or factor |
The list of comparisons for node-splitting is generated following the algorithm of \insertCiteValkenhoef2016;textualmultinma. A comparison between two treatments has the potential for inconsistency, and is thus considered for node-splitting, if the comparison has both direct evidence and independent indirect evidence.
The notion of independent indirect evidence is necessary when multi-arm
trials are present, since by design these trials are internally consistent.
A comparison between two treatments has independent indirect evidence if,
after removing all studies comparing the two treatments from the network,
the two treatments are still connected by a path of evidence. This is the
criterion considered by the has_indirect()
function.
For has_direct()
and has_indirect()
, a single logical value. For
get_nodesplits()
, a data frame with two columns giving the comparisons
for node-splitting.
# Parkinsons example
park_net <- set_agd_arm(parkinsons,
study = studyn,
trt = trtn,
y = y,
se = se,
trt_ref = 1)
# View the network plot
plot(park_net)
# The 4 vs. 5 comparison is a spur on the network
has_direct(park_net, 4, 5)
has_indirect(park_net, 4, 5)
# 1 and 5 are not directly connected
has_direct(park_net, 1, 5)
has_indirect(park_net, 1, 5)
# The 1 vs. 2 comparison does not have independent indirect evidence, since
# the 1-2-4 loop is a multi-arm study
has_indirect(park_net, 1, 2)
# Get a list of comparisons with potential inconsistency for node-splitting
get_nodesplits(park_net)
# See van Valkenhoef (2016) for a discussion of this example
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