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Target Trial Emulation Without Matching: A More Efficient Approach for Evaluating Vaccine Effectiveness Using Observational Data.

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Τίτλος: Target Trial Emulation Without Matching: A More Efficient Approach for Evaluating Vaccine Effectiveness Using Observational Data.
Συγγραφείς: Wu E; From the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA., Rogawski McQuade E; Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA., Stensrud MJ; Department of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland., Nabi R; From the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA., Benkeser D; From the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA.
Πηγή: Epidemiology (Cambridge, Mass.) [Epidemiology] 2026 Sep 01; Vol. 37 (5), pp. 612-619. Date of Electronic Publication: 2026 Mar 30.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 9009644 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1531-5487 (Electronic) Linking ISSN: 10443983 NLM ISO Abbreviation: Epidemiology Subsets: MEDLINE
Imprint Name(s): Publication: <2000>- : Hagerstown, MD : Lippincott Williams & Wilkins
Original Publication: [Cambridge, MA : Blackwell Scientific Publications ; Chestnut Hill, MA : Epidemiology Resources, c1990-
Ιατρικοί όροι (MeSH): Vaccine Efficacy*/statistics & numerical data , COVID-19*/prevention & control , COVID-19*/epidemiology , COVID-19 Vaccines*/therapeutic use , Observational Studies as Topic*, Humans ; Child ; Child, Preschool ; Proportional Hazards Models ; SARS-CoV-2 ; Computer Simulation
Περίληψη: Real-world vaccine effectiveness (VE) has increasingly been studied using matching-based approaches, particularly in observational cohort studies that follow the target trial emulation framework. Although matching is appealing in its simplicity, it has important limitations in terms of clarity of the target estimand and the precision with which it is estimated. Moreover, defining causal estimands of VE requires care, because vaccine uptake often occurs over calendar time when infection dynamics may also be rapidly changing. We propose a causal estimand of VE that summarizes VE over calendar time, similar to how vaccine efficacy is summarized in randomized controlled trials. We describe the identification of our estimand and propose simple-to-implement estimators based on two hazard regression models. We apply our proposed estimator in simulations and in a study assessing the effectiveness of the Pfizer-BioNTech COVID-19 vaccine to prevent SARS-CoV-2 infections in children 5-11 years old. In both settings, we find that our proposed estimator yields similar scientific inferences while providing significant efficiency gains over commonly used matching-based estimators.
(Copyright © 2026 Wolters Kluwer Health, LLC. All rights reserved.)
Competing Interests: Disclosure: The authors report no conflicts of interest.
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Contributed Indexing: Keywords: COVID-19; Causal inference; Cohort design; Estimands; Matching; Target trial emulation; Vaccine effectiveness
Substance Nomenclature: 0 (COVID-19 Vaccines)
Entry Date(s): Date Created: 20260330 Date Completed: 20260729 Latest Revision: 20260801
Update Code: 20260801
DOI: 10.1097/EDE.0000000000001982
PMID: 41911278
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1531-5487
DOI:10.1097/EDE.0000000000001982