Estimating the contribution of studies in network meta-analysis: paths, flows and streams.

ABSTRACT: 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 proportion 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 proportion 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 proportion contributions based on the observation that the rows of H  

SUBMITTER: Papakonstantinou T 

PROVIDER: S-EPMC6148216 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature