| Autor: |
Jessica L Harding, Emily Pfaff, Edward Boyko, Pandora L. Wander |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Diabetes Epidemiology and Management, Vol 14, Iss , Pp 100193- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2666-9706 |
| DOI: |
10.1016/j.deman.2023.100193 |
| Popis: |
Observational studies based on cohorts built from electronic health records (EHR) form the backbone of our current understanding of the risk of new-onset diabetes following COVID. EHR-based research is a powerful tool for medical research but is subject to multiple sources of bias. In this viewpoint, we define key sources of bias that threaten the validity of EHR-based research on this topic (namely misclassification, selection, surveillance, immortal time, and confounding biases), describe their implications, and suggest best practices to avoid them in the context of COVID-diabetes research. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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