Publication

Journal	F1000Research
Volume (Issue)	7
Page(s)	610 - 610
Title of proceedings	F1000Research
DOI	10.12688/f1000research

URL	https://f1000researchdata.s3.amazonaws.com/manuscripts/16071/225f941b-9fae-4615-9b6b-caddf3983b91_14770_-_adriani_nikolakopoulou.pdf?doi=10.12688/f1000research.14770.1&numberOfBrowsableCollections=24&numberOfBrowsableInstitutionalCollections=5&numberOfBrowsableGateways=24
Type of Open Access	Publisher (Gold Open Access)

In network meta-analysis, it is important to assess the influence of the limitations or other characteristics of individual studies on the estimates obtained from the network. The percentage contribution matrix, which shows how much each direct treatment effect contributes to each treatment effect estimate from network meta-analysis, is crucial in this context. We use ideas from graph theory to derive the percentage that is contributed by each direct treatment effect. We start with the ‘projection’ matrix in a two-step network meta-analysis model, called the H matrix, which is analogous to the hat matrix in a linear regression model. We develop a method to translate H entries to percentage contributions based on the observation that the rows of H can be interpreted as flow networks, where a stream is defined as the composition of a path and its associated flow. We present an algorithm that identifies the flow of evidence in each path and decomposes it into direct comparisons. To illustrate the methodology, we use two published networks of interventions. The first compares no treatment, quinolone antibiotics, non-quinolone antibiotics and antiseptics for underlying eardrum perforations and the second compares 14 antimanic drugs. We believe that this approach is a useful and novel addition to network meta-analysis methodology, which allows the consistent derivation of the percentage contributions of direct evidence from individual studies to network treatment effects.