Methods to Adjust for Confounding in Test-Negative Design COVID-19 Effectiveness Studies: Simulation Study

Overall, models using a DRS to adjust for confounding performed adequately but not as well as the multivariable models that adjusted for covariates individually.

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Publication Details

Journal
JMIR Formative Research
Published
2025-01-27
DOI
https://doi.org/10.2196/58981
Citations
2
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
5.26
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article

Methods to Adjust for Confounding in Test-Negative Design COVID-19 Effectiveness Studies: Simulation Study

William F. Fadel, Toan C. Ong, Allison L. Naleway, Inih Essien et al.
2 citations
JMIR Formative Research
Advanced Causal Inference Techniques
5.26
article

Methods to Adjust for Confounding in Test-Negative Design COVID-19 Effectiveness Studies: Simulation Study

William F. Fadel, Toan C. Ong, Allison L. Naleway, Inih Essien, Peter J. Embí, Brian E. Dixon, Ruth Link‐Gelles, Ryan E. Wiegand, Mark W. Tenforde, Elizabeth A. K. Rowley, Edward Stenehjem, Bruce Fireman, Karthik V. Natarajan, Manjusha Gaglani, Patrick K. Mitchell, Gabriela Vazquez‐Benitez, Ned Lewis, Duck‐Hye Yang
article en
2 citations

Abstract

Overall, models using a DRS to adjust for confounding performed adequately but not as well as the multivariable models that adjusted for covariates individually.

JMIR Formative ResearchVol. 9
Intermountain Healthcare (US), Centers for Disease Control and Prevention (US), Regenstrief Institute (US), Westat (United States) (US), NewYork–Presbyterian Hospital (US), Kaiser Permanente (US), National Center for Immunization and Respiratory Diseases (US), Columbia University Irving Medical Center (US), Kaiser Permanente Center for Health Research (US), Baylor Scott & White Health (US), University of Colorado Anschutz Medical Campus (US), HealthPartners (US), Indiana University – Purdue University Indianapolis (US), Texas A&M University – Central Texas (US), Vanderbilt University Medical Center (US)
Good health and well-being
Openalex Percentile: Top 6%
Advanced Causal Inference Techniques
5.26
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