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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 van Valkenhoef van Valkenhoef et al. (2016) .

Usage

inconsistency.loops(data)

Arguments

data

A data frame containing variables studyID and treatment (as numeric codes) that indicate which treatments are used in which studies.

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.

Details

Similar to gemtc::mtc.nodesplit() but uses a fixed reference treatment and therefore suggests 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.

References

van Valkenhoef G, Dias S, Ades AE, Welton NJ (2016). “Automated generation of node-splitting models for assessment of inconsistency in network meta-analysis.” Res Synth Methods, 7(1), 80-93. ISSN 1759-2887 (Electronic) 1759-2879 (Linking), doi:10.1002/jrsm.1167 , https://pubmed.ncbi.nlm.nih.gov/26461181/.

Examples

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)
  )

# Identify comparisons informed by direct and indirect evidence
inconsistency.loops(data)
#>   t1 t2    path
#> 5  3  4 3->1->4
#> 4  2  3 2->1->3