This paper is in the following e-collection/theme issue:

Statistical Methods for Surveillance and Population Health (158) JMIR Theme Issue: COVID-19 Special Issue (2461) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1520) Outbreak and Pandemic Preparedness and Management (1743) Infectious Diseases (non-STD/STI) (1592) Validity, Reliability, and Bias (94)

Published on in Vol 8, No 7 (2022): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31306, first published .
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

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

Authors of this article:

Nathaniel Stockham1 Author Orcid Image ;   Peter Washington2 Author Orcid Image ;   Brianna Chrisman2 Author Orcid Image ;   Kelley Paskov3 Author Orcid Image ;   Jae-Yoon Jung4 Author Orcid Image ;   Dennis Paul Wall4, 5 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. Washington P. A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health. Journal of Medical Internet Research 2024;26:e51138 View
  2. Martell M, Terry N, Sengupta R, Salazar C, Errett N, Miles S, Wartman J, Choe Y, Mosa A. Open-source data pipeline for street-view images: A case study on community mobility during COVID-19 pandemic. PLOS ONE 2024;19(5):e0303180 View