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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...
Autores principales: | Stockham, Nathaniel, Washington, Peter, Chrisman, Brianna, Paskov, Kelley, Jung, Jae-Yoon, Wall, Dennis Paul |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
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Materias: | |
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 |
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