Causal Modeling to Mitigate Selection Bias and Unmeasured Confounding in Internet-Based Epidemiology of COVID-19: Model Development and Validation

BACKGROUND: Selection bias and unmeasured confounding are fundamental problems in epidemiology that threaten study internal and external validity. These phenomena are particularly dangerous in internet-based public health surveillance, where traditional mitigation and adjustment methods are inapplic...

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Detalles Bibliográficos
Autores principales: Stockham, Nathaniel, Washington, Peter, Chrisman, Brianna, Paskov, Kelley, Jung, Jae-Yoon, Wall, Dennis Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9307267/
https://www.ncbi.nlm.nih.gov/pubmed/35605128
http://dx.doi.org/10.2196/31306

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9307267/
https://www.ncbi.nlm.nih.gov/pubmed/35605128
http://dx.doi.org/10.2196/31306

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