Combining Observational Studies to Reduce Multiple Biases.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Combining Observational Studies to Reduce Multiple Biases.
Συγγραφείς:	Cole SR; From the Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC., Zivich PN; From the Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC., Shook-Sa BE; Department of Biostatistics, UNC Gillings School of Global Public Health, Chapel Hill, NC.; Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom., Richardson DB; Department of Environmental and Occupational Health, UC Irvine Wen School of Population and Public Health, Irvine, CA., Hudgens MG; Department of Biostatistics, UNC Gillings School of Global Public Health, Chapel Hill, NC., Edwards JK; From the Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC.
Πηγή:	Epidemiology (Cambridge, Mass.) [Epidemiology] 2026 Sep 01; Vol. 37 (5), pp. 565-574. Date of Electronic Publication: 2026 May 15.
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):	Observational Studies as Topic*/methods, Bias ; Monte Carlo Method ; Humans ; Confounding Factors, Epidemiologic
Περίληψη:	Epidemiology stands to benefit greatly from combining data sources with complementary strengths. We propose a study design and estimators to combine information from multiple observational studies to simultaneously address confounding and outcome measurement error. Using inverse probability weighted, g-computation, and augmented inverse probability weighted estimators, we show how to combine information from two studies wherein the first study is subject to outcome misclassification (but has adequate confounder control) and the second study has gold-standard outcomes (but inadequate confounder control). Monte Carlo experiments demonstrate that the proposed estimators remove both confounding and measurement biases and provide appropriate 95% confidence interval coverage, while standard analyses fall short. Fusion designs offer a principled approach to combine data from multiple sources to address multiple biases in epidemiologic research. (Copyright © 2026 Wolters Kluwer Health, LLC. All rights reserved.)
Competing Interests:	Disclosure: The authors report no conflicts of interest.
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Grant Information:	K01 AI177102 United States AI NIAID NIH HHS; K01 AI182506 United States AI NIAID NIH HHS; P30 AI050410 United States AI NIAID NIH HHS; R01 AI157758 United States AI NIAID NIH HHS
Contributed Indexing:	Keywords: Bias; Cohort Study; Confounding; Data Fusion; Outcome Measurement Error; Random Error
Entry Date(s):	Date Created: 20260515 Date Completed: 20260729 Latest Revision: 20260801
Update Code:	20260801
PubMed Central ID:	PMC13182983
DOI:	10.1097/EDE.0000000000001999
PMID:	42138357
Βάση Δεδομένων:	MEDLINE

Περιγραφή
ISSN:	1531-5487
DOI:	10.1097/EDE.0000000000001999