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Cited by 235

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Case Series Analysis of Adverse Reactions to Vaccines: A Comparative Evaluation

Farrington, C. P., Nash, J., Miller, E.·1996·American Journal of Epidemiology

Abstract

A modified cohort method has been proposed for estimating the relative incidence of rare adverse reactions after vaccination. The method requires only a sample of the cases, thus avoiding the need for following large population cohorts or selecting controls. This case series method has statistical power equivalent to that of the full cohort method when the risk periods after vaccination are short and vaccine coverage is high. The method also eliminates confounding by variables associated with both the outcome and avoidance of vaccination. In this paper, the cohort, case-control, and case series methods are reviewed, and their underlying assumptions and performances are compared. Theoretical results are illustrated using data on febrile convulsions after measles-mumps-rubella vaccination in the United Kingdom.

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Metadata

Authors
Farrington, C. P., Nash, J., & Miller, E.
Venue
American Journal of Epidemiology
Published
1996-06-01
Volume
143
Issue
11
Pages
1165-1173
ISSN
  • 0002-9262 (ISSN-L)
  • 1476-6256
OpenAlex
W1991473061
Cited by
235
References
2
FWCI
2.93
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Case Series Analysis of Adverse Reactions to Vaccines: A Comparative Evaluation. https://app.cochat.ai/papers/10.1093/oxfordjournals.aje.a008695
The paper
Farrington, C. P., Nash, J., & Miller, E. (1996). Case Series Analysis of Adverse Reactions to Vaccines: A Comparative Evaluation. American Journal of Epidemiology. https://doi.org/10.1093/oxfordjournals.aje.a008695

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.