| Autor: |
Robert J. Reynolds, Ryan T. Scott, Russell T. Turner, Urszula T. Iwaniec, Mary L. Bouxsein, Lauren M. Sanders, Erik L. Antonsen |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Biomedicines, Vol 10, Iss 9, p 2187 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2227-9059 |
| DOI: |
10.3390/biomedicines10092187 |
| Popis: |
As part of the risk management plan for human system risks at the US National Aeronautics and Space Administration (NASA), the NASA Human Systems Risk Board uses causal diagrams (in the form of directed, acyclic graphs, or DAGs) to communicate the complex web of events that leads from exposure to the spaceflight environment to performance and health outcomes. However, the use of DAGs in this way is relatively new at NASA, and thus far, no method has been articulated for testing their veracity using empirical data. In this paper, we demonstrate a set of procedures for doing so, using (a) a DAG related to the risk of bone fracture after exposure to spaceflight; and (b) four datasets originally generated to investigate this phenomenon in rodents. Tests of expected marginal correlation and conditional independencies derived from the DAG indicate that the rodent data largely agree with the structure of the diagram. Incongruencies between tests and the expected relationships in one of the datasets are likely explained by inadequate representation of a key DAG variable in the dataset. Future directions include greater tie-in with human data sources, including multiomics data, which may allow for more robust characterization and measurement of DAG variables. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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