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Estimating the contribution of studies in network meta-analysis: paths, flows and streams
Theodoros Papakonstantinou1, Adriani Nikolakopoulou1, Gerta Rücker2
1Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
F1000Research
|September 27, 2019
Summary
This study introduces a novel graph theory method to quantify how individual study results influence network meta-analysis estimates. The new approach accurately measures the proportion contribution of direct treatment effects within complex evidence networks.
Area of Science:
- Biostatistics
- Medical Informatics
- Network Science
Background:
- Assessing study influence is vital in network meta-analysis.
- The proportion contribution matrix is key for understanding this influence.
Purpose of the Study:
- To develop a graph theory-based method for deriving proportion contributions in network meta-analysis.
- To provide a consistent approach for quantifying direct evidence's impact on network estimates.
Main Methods:
- Utilized graph theory concepts and the 'projection' matrix from two-step network meta-analysis models.
- Interpreted matrix rows as flow networks to derive proportion contributions.
- Developed an algorithm to identify and decompose evidence flow in network paths.
Main Results:
- Successfully translated matrix entries into proportion contributions.
- Demonstrated the methodology using two distinct intervention networks (eardrum perforations and antimanic drugs).
- The approach allows consistent derivation of direct evidence proportions.
Conclusions:
- The proposed graph theory method offers a novel and useful addition to network meta-analysis.
- This technique enhances the transparency and reliability of network meta-analysis findings by quantifying study contributions.
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