ArticlemedRxiv : the preprint server for health sciences2025
Causal Inference via Electronic Health Records in the National Clinical Cohort Collaborative: Challenges and Solutions in Long COVID Research.
Zachary Butzin-Dozier, Yunwen Ji, Lin-Chiun Wang, A Jerrod Anzalone, Eric Hurwitz, Rena C Patel, Mark J van der Laan, John M Colford, Alan E Hubbard
Abstract readPreprint
In one paragraphArticle in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
9 authors.
Yunwen JiSchool of Public Health, University of California, Berkeley, Berkeley, CA USA.
Lin-Chiun WangSchool of Public Health, University of California, Berkeley, Berkeley, CA USA.
A Jerrod AnzaloneUniversity of Nebraska Medical Center, Omaha, NE USA.
Eric HurwitzUniversity of North Carolina at Chapel Hill, Chapel Hill, NC USA.
Rena C PatelUniversity of Alabama at Birmingham, Birmingham, AL USA.
Mark J van der LaanSchool of Public Health, University of California, Berkeley, Berkeley, CA USA.
John M ColfordSchool of Public Health, University of California, Berkeley, Berkeley, CA USA.
Funding
Vanderbilt Institute for Clinical and Translational Research (VICTR) -Identifying correlates of functional immunity in SARS-CoV-2 convalescent plasmaUL1TR002243 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Paul A. Harris, Wesley H Self · 2017 to 2026
$130.7MUniversity of Pittsburgh Clinical and Translational Science InstituteUL1TR001857 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E · 2016 to 2025
$129.3MUCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1MClinical and Translational Science InstituteUL1TR001872 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI COLLARD, HAROLD R, JACOBY, VANESSA · 2016 to 2025
$112.1MProject-005UL1TR001445 · NCATS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI BREDELLA, MIRIAM ANTOINETTE, HOCHMAN, JUDITH S · 2015 to 2025
$103.5MYale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9MPhenotypic Diversity in COVID-19UL1TR001878 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI FITZGERALD, GARRET A · 2016 to 2025
$102.4MTransform Dissemination and Implementation Science in CTSA ProgramsUL1TR002319 · NCATS · UNIVERSITY OF WASHINGTON · PI John K. Amory · 2017 to 2026
$100.0MClinical and Translational Science AwardUL1TR001873 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI REILLY, MUREDACH P · 2016 to 2025
$99.0MWU INSTITUTE OF CLINICAL AND TRANSLATIONAL SCIENCESUL1TR002345 · NCATS · WASHINGTON UNIVERSITY · PI William G. Powderly · 2017 to 2026
$97.8MThe Harvard Clinical and Translational Science CenterUL1TR002541 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2018 to 2022
$93.0MImplementing a Maternal health and PRegnancy Outcomes Vision for Everyone (IMPROVE)UL1TR002378 · NCATS · EMORY UNIVERSITY · PI Andres J Garcia, Elizabeth O. Ofili · 2017 to 2026
$92.1MCLC NIH HHS 75N90023D00001Gates Foundation INV-018455NCATS NIH HHS U24 TR002306NCATS NIH HHS UL1 TR001409NCATS NIH HHS UL1 TR001412NCATS NIH HHS UL1 TR001414NCATS NIH HHS UL1 TR001420NCATS NIH HHS UL1 TR001422NCATS NIH HHS UL1 TR001425NCATS NIH HHS UL1 TR001427NCATS NIH HHS UL1 TR001430NCATS NIH HHS UL1 TR001433NCATS NIH HHS UL1 TR001436NCATS NIH HHS UL1 TR001439NCATS NIH HHS UL1 TR001442NCATS NIH HHS UL1 TR001445NCATS NIH HHS UL1 TR001449NCATS NIH HHS UL1 TR001450NCATS NIH HHS UL1 TR001453NCATS NIH HHS UL1 TR001855NCATS NIH HHS UL1 TR001857NCATS NIH HHS UL1 TR001860NCATS NIH HHS UL1 TR001863NCATS NIH HHS UL1 TR001866NCATS NIH HHS UL1 TR001872NCATS NIH HHS UL1 TR001873NCATS NIH HHS UL1 TR001876NCATS NIH HHS UL1 TR001878NCATS NIH HHS UL1 TR001881NCATS NIH HHS UL1 TR001998NCATS NIH HHS UL1 TR002001NCATS NIH HHS UL1 TR002003NCATS NIH HHS UL1 TR002014NCATS NIH HHS UL1 TR002240NCATS NIH HHS UL1 TR002243NCATS NIH HHS UL1 TR002319NCATS NIH HHS UL1 TR002345NCATS NIH HHS UL1 TR002366NCATS NIH HHS UL1 TR002369NCATS NIH HHS UL1 TR002373NCATS NIH HHS UL1 TR002377NCATS NIH HHS UL1 TR002378NCATS NIH HHS UL1 TR002384NCATS NIH HHS UL1 TR002389NCATS NIH HHS UL1 TR002489NCATS NIH HHS UL1 TR002494NCATS NIH HHS UL1 TR002529NCATS NIH HHS UL1 TR002535NCATS NIH HHS UL1 TR002537NCATS NIH HHS UL1 TR002538NCATS NIH HHS UL1 TR002541NCATS NIH HHS UL1 TR002544NCATS NIH HHS UL1 TR002548NCATS NIH HHS UL1 TR002550NCATS NIH HHS UL1 TR002553NCATS NIH HHS UL1 TR002556NCATS NIH HHS UL1 TR002645NCATS NIH HHS UL1 TR002649NCATS NIH HHS UL1 TR002733NCATS NIH HHS UL1 TR002736NCATS NIH HHS UL1 TR003015NCATS NIH HHS UL1 TR003017NCATS NIH HHS UL1 TR003096NCATS NIH HHS UL1 TR003098NCATS NIH HHS UL1 TR003107NCATS NIH HHS UL1 TR003142NCATS NIH HHS UL1 TR003167NCATS NIH HHS UM1 TR004406NCATS NIH HHS UM1 TR004528NHLBI NIH HHS 75N92023D00001NIAID NIH HHS K01 AI182501NIDA NIH HHS 75N95021D00001NIDA NIH HHS U01 DA055358NIGMS NIH HHS U54 GM104938NIGMS NIH HHS U54 GM104940NIGMS NIH HHS U54 GM104941NIGMS NIH HHS U54 GM104942NIGMS NIH HHS U54 GM115371NIGMS NIH HHS U54 GM115428NIGMS NIH HHS U54 GM115458NIGMS NIH HHS U54 GM115516NIGMS NIH HHS U54 GM115677NIGMS NIH HHS U54 GM133807ORFDO NIH HHS 75N99023D00001
6 · The paper itselfAbstract
Observational analyses of electronic health record (EHR) data using databases such as the National Clinical Cohort Collaborative include unique challenges for researchers seeking causal inferences, particularly when evaluating subjectively-defined outcomes like Long COVID. We explore several challenges and describe potential solutions. 1. Lack of true negatives: Many diagnoses and conditions either have a positive indicator or a missing status, requiring investigators to carefully consider which patients are likely negative for this condition. 2. Differential monitoring: EHR data include nonrandom missingness driven by patients engaging with the healthcare system at different rates, which is often related to both the exposure and outcome of interest. 3. Bias: EHR data sources face many biases, but are particularly vulnerable to informative missingness, differential monitoring, and model misspecification. 4. Large sample size: High precision (i.e., narrow confidence intervals) paired with potential bias leads to a high risk of incorrectly rejecting the null hypothesis. 5. Defining index time: It is important that investigators deliberately define index time (i.e.,
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PMID40502605
PMCPMC12155030
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