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Advances in methodologies of negative controls: a scoping review.
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- Author(s): Yang Q;Yang Q; Yang Z; Yang Z; Yang Z; Cai X; Cai X; Zhao H; Zhao H; Jia J; Jia J; Jia J; Sun F; Sun F; Sun F
- Source:
Journal of clinical epidemiology [J Clin Epidemiol] 2024 Feb; Vol. 166, pp. 111228. Date of Electronic Publication: 2023 Nov 30.- Publication Type:
Journal Article; Review- Language:
English - Source:
- Additional Information
- Source: Publisher: Elsevier Country of Publication: United States NLM ID: 8801383 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-5921 (Electronic) Linking ISSN: 08954356 NLM ISO Abbreviation: J Clin Epidemiol Subsets: MEDLINE
- Publication Information: Publication: New York : Elsevier
Original Publication: Oxford ; New York : Pergamon Press, c1988- - Subject Terms:
- Abstract: Objectives: Negative controls are considered an important tool to mitigate biases in observational studies. The aim of this scoping review was to summarize current methodologies of negative controls (both negative control exposure [NCE] and negative control outcome [NCO]).
Study Design and Setting: We searched PubMed, Web of Science, Embase, and Cochrane Library (up to March 9, 2023) for articles on methodologies of negative controls. Two reviewers selected eligible studies and collected relevant data independently and in duplicate. We reported total numbers and percentages, and summarized methodologies narratively.
Results: A total of 37 relevant methodological articles were included in our review. These publications covered NCE (n = 11, 29.8%), NCO (n = 13, 35.1%), or both (n = 13, 35.1%), with most focused on bias detection (n = 14, 37.8%), bias correction (n = 16, 43.3%), and P value or confidence interval (CI) calibration (n = 5, 13.5%). For the two remaining articles (5.4%), one discussed bias detection and P value or CI calibration and the other covered all the three functions. For bias detection, the existence of an association between the NCE (NCO) and outcome (exposure) variables of interest simply indicates that results may suffer from confounding bias, selection bias and/or information bias. For bias correction, however, the algorithms of negative control methods need more stringent assumptions such as rank preservation, monotonicity, and linearity.
Conclusion: Negative controls can be leveraged for bias detection, P value or CI calibration, and bias correction, among which bias correction has been the most studied methodologically. The current available methods need some stringent assumptions to detect or remove bias. More methodological research is needed to optimize the use of negative controls.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.) - Contributed Indexing: Keywords: Causal inference; Methodology; Negative control exposure; Negative control outcome; Scoping review; Unmeasured confounding
- Publication Date: Date Created: 20231201 Date Completed: 20240311 Latest Revision: 20240402
- Publication Date: 20240403
- Accession Number: 10.1016/j.jclinepi.2023.111228
- Accession Number: 38040387
- Source:
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