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

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 adjustmen...

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Bibliographic Details
Main Authors: Nathaniel Stockham, Peter Washington, Brianna Chrisman, Kelley Paskov, Jae-Yoon Jung, Dennis Paul Wall
Format: Article
Language:English
Published: JMIR Publications 2022-07-01
Series:JMIR Public Health and Surveillance
Online Access:https://publichealth.jmir.org/2022/7/e31306

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