inconsistency.loops {MBNMAdose} | R Documentation |
Identify comparisons in loops that fulfill criteria for node-splitting
Description
Identify comparisons informed by both direct and indirect evidence from independent sources, which therefore fulfill the criteria for testing for inconsistency via node-splitting.
Usage
inconsistency.loops(df, checkindirect = TRUE, incldr = FALSE)
Arguments
df |
A data frame containing variables |
checkindirect |
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.
Default is |
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. |
Details
Similar to 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
igraph
.
Value
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 t1
and t2
correspond
to treatment codes. path
indicates the treatment codes that connect the
shortest path of indirect evidence.
If incldr=TRUE
then 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 t1
and t2
respectively/
References
There are no references for Rd macro \insertAllCites
on this help page.
Examples
# Identify comparisons informed by direct and indirect evidence
#in triptans dataset
network <- mbnma.network(triptans)
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)