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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2604.16221 |
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| _version_ | 1866918452616232960 |
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| author | Rücker, Gerta Davies, Annabel L. Schwarzer, Guido |
| author_facet | Rücker, Gerta Davies, Annabel L. Schwarzer, Guido |
| contents | We show that the covariance matrix of the treatment effect estimates in a network meta-analysis can be obtained without matrix inversion using a geometric series of diffusion matrices. This property extends to the hat matrix and provides a connection between parameter estimation in regression analysis and random walks on the network graph. We also provide a number of visualization tools implemented in R. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_16221 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Network Meta-analysis and Diffusion Rücker, Gerta Davies, Annabel L. Schwarzer, Guido Methodology We show that the covariance matrix of the treatment effect estimates in a network meta-analysis can be obtained without matrix inversion using a geometric series of diffusion matrices. This property extends to the hat matrix and provides a connection between parameter estimation in regression analysis and random walks on the network graph. We also provide a number of visualization tools implemented in R. |
| title | Network Meta-analysis and Diffusion |
| topic | Methodology |
| url | https://arxiv.org/abs/2604.16221 |