Besides augmenting validity of comparisons between available treatments through including indirect evidence, network meta-analyses allow for a formal assessment of evidence inconsistencies.
In the presence of heterogeneity, incorporating information on either a) patients' baseline characteristics or b) control for treatment-effect modifiers within the synthesis model has been shown to improve estimates and, by doing so, possibly resolve evidence inconsistencies [ 6, 34].
Heterogeneity between trials was low (τ=0.02), there was no evidence for inconsistency (inconsistency factor −0.1 mm, −0.6 to 0.4 mm; P=0.54), and the goodness of fit of the model to the data was excellent.
Heterogeneity between trials was low (τ=0.04 for heterogeneity between trials on a 10 cm visual analogue scale), there was no evidence for inconsistency (inconsistency factor 0.2 cm, −0.7 to 1.1, P=0.63), and the goodness of fit of the model to the data was excellent (data available on request).
There was no evidence of inconsistency for the primary outcome of overall mortality (P for inconsistency=0.98), and 95% credibility intervals of all loop specific inconsistency factors overlapped the null (see supplementary figure 3).
There is evidence of inconsistency.
These results add further evidence of inconsistency between historical plagues, including the Black Death, and our current understanding of Y. pestis-variant disease.
In addition, there was no evidence of inconsistency between direct and indirect evidence.
There was no other evidence of inconsistency in snapshot treatment differences across other subgroups.
Model fit statistics indicated evidence of inconsistency for this network (appendix 6, table 9).
