Causal Modeling to Mitigate Selection Bias and Unmeasured Confounding in Internet-Based Epidemiology of COVID-19: Model Development and Validation
BackgroundSelection 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 inapplicab...
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| Main Authors: | , , , , , |
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| Format: | Book |
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JMIR Publications,
2022-07-01T00:00:00Z.
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A1234.567 |
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