Identify comparisons informed by both direct and indirect evidence from independent sources, which therefore fulfil the criteria for testing for inconsistency via node-splitting. Follows the method of \insertCitevanvalkenhoef2016;textualMBNMAdose.
A data frame containing variables
A boolean object to indicate whether or not to perform an additional
check to ensure network remains connected even after dropping direct evidence on a comparison.
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.
gemtc::mtc.nodesplit.comparisons() but uses a fixed
reference treatment and therefore identifies fewer loops in which to test for
inconsistency. Heterogeneity can also be parameterised as inconsistency and
so testing for inconsistency in additional loops whilst changing the
reference treatment would also be identifying heterogeneity. Depends on
A data frame of comparisons that are informed by direct and indirect
evidence from independent sources. Each row of the data frame is a
different treatment comparison. Numerical codes in
to treatment codes.
path indicates the treatment codes that connect the
shortest path of indirect evidence.
path may indicate
doseresp for some comparisons.
These are comparisons for which indirect evidence is only available via the
dose-response relationship. The two numbers given after (e.g.
3 2) indicate the
number of doses available in the indirect evidence with which to estimate the
dose-response function for the treatments in
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
# Identify comparisons informed by direct and indirect evidence #in triptans dataset network <- mbnma.network(HF2PPITT) inconsistency.loops(network$data.ab) # Include indirect evidence via dose-response relationship inconsistency.loops(network$data.ab, incldr=TRUE) # Do not perform additional connectivity check on data data <- data.frame(studyID=c(1,1,2,2,3,3,4,4,5,5,5), treatment=c(1,2,1,3,2,3,3,4,1,2,4) ) inconsistency.loops(data, checkindirect=FALSE)
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