94 A key assumption behind multiple treatment comparison meta-ana

94 A key assumption behind multiple treatment comparison meta-analysis is that the analysed network is consistent or coherent, that is, that direct and indirect evidence on the same comparisons do not disagree beyond chance. We will identify and estimate incoherence by employing a mixed treatment Src Bosutinib comparisons incoherence model in the Bayesian framework.95 For each comparison, we will note the direct estimates and associated CIs from the previous analysis and calculate the indirect estimate using a node splitting procedure as well as the network estimate. We will conduct a statistical test for incoherence between the direct and the indirect

estimate. We will have assessed confidence in estimates of effect from the direct comparisons in our pair-wise meta-analyses described previously. For rating confidence in the indirect comparisons, we will focus our assessments on first-order

loops (ie, loops that are connected to the interventions of interest through only one other intervention; eg, A vs C and B vs C to estimate effects of A vs B) with the lowest variances, and thus contribute the most to the estimates of effect. Within each loop, our confidence in the indirect comparison will be the lowest of the confidence ratings we have assigned to the contributing direct comparisons. For instance, if treatment A versus C warrants high confidence and B versus C warrants moderate confidence,

we will judge the associated indirect comparison (A vs B) as warranting moderate confidence. We may rate down confidence in the indirect comparisons further if we have a strong suspicion that the transitivity assumption (ie, the assumption that there are no effect modifiers—such as differences in patients, extent to which interventions have been optimally administered, differences in the comparator, and differences in how the outcome has been measured—in the two direct comparisons that may bias the indirect estimate) has been violated. Our overall judgement of confidence in the network estimate for any paired comparison will be the higher of the confidence rating among the contributing direct and indirect comparisons. However, we Anacetrapib may rate down confidence in the network estimate if we find that the direct and indirect estimates are incoherent. As a secondary analysis, we will rank the interventions using the SUCRA (surface under the cumulative ranking) method.96 The SUCRA rankings may be misleading: if there is only evidence warranting low confidence for most comparisons; if the evidence supporting the higher ranked interventions warrants lower confidence than the evidence supporting the lower ranked interventions; or if the magnitude of effect is very similar in higher versus lower ranked comparisons. We will consider these issues in interpreting the SUCRA rankings.

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