Differential network testing reveals diverging dynamics of organ system interactions for survivors and non-survivors in intensive care medicine
Statistical network analyses have become popular in many scientific disciplines, where an important task is to test for differences between two networks. We describe an overall framework for differential network testing procedures that vary regarding (1) the network estimation method, typically base...
Gespeichert in:
| Hauptverfasser: | , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2022
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| In: |
Frontiers in physiology
Year: 2022, Jahrgang: 12, Pages: 1-14 |
| ISSN: | 1664-042X |
| DOI: | 10.3389/fphys.2021.801622 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.3389/fphys.2021.801622 Verlag, kostenfrei, Volltext: https://www.frontiersin.org/article/10.3389/fphys.2021.801622 
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| Verfasserangaben: | Roman Schefzik, Leonie Boland, Bianka Hahn, Thomas Kirschning, Holger A. Lindner, Manfred Thiel and Verena Schneider-Lindner |
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| 520 | |a Statistical network analyses have become popular in many scientific disciplines, where an important task is to test for differences between two networks. We describe an overall framework for differential network testing procedures that vary regarding (1) the network estimation method, typically based on specific concepts of association, and (2) the network characteristic employed to measure the difference. Using permutation-based tests, our approach is general and applicable to various overall, node-specific or edge-specific network difference characteristics. The methods are implemented in our freely available R software package DNT, along with an R Shiny application. In a study in intensive care medicine, we compare networks based on parameters representing main organ systems to evaluate the prognosis of critically ill patients in the intensive care unit (ICU), using data from the surgical ICU of the University Medical Centre Mannheim, Germany. We specifically consider both cross-sectional comparisons between a non-survivor and a survivor group and longitudinal comparisons at two clinically relevant time points during the ICU stay: first, at admission, and second, at an event stage prior to death in non-survivors or a matching time point in survivors. The non-survivor and the survivor networks do not significantly differ at the admission stage. However, the organ system interactions of the survivors then stabilize at the event stage, revealing significantly more network edges, whereas those of the non-survivors do not. In particular, the liver appears to play a central role for the observed increased connectivity in the survivor network at the event stage. | ||
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