Case-non-case studies: principle, methods, bias and interpretation
Open accessAbstract
Case-non-case studies are among the methods used to assess drug safety by analyzing the disproportionality of adverse drug reaction reports in pharmacovigilance databases. First used in the 1980s, the last few decades have seen a significant increase in the use of this study design. The principle of case-non-case studies is to compare the drug exposure of cases of an adverse reaction of interest with that of cases with other reported reactions and called "non-cases". Their results are presented in the form of reporting odds ratio (ROR), the interpretation of which makes it possible to highlight pharmacovigilance signals. This article describes the principle of case-non-case studies, the calculation method of the ROR and its confidence interval, the different analytical modalities and how to interpret its results with regard to the advantages and limitations of this type of study.
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Disproportionality analysis is a validated concept in pharmacovigilance.
“We performed the so-called disproportionality approach, a validated concept in pharmacovigilance, to assess whether suspected adrenal AEs (cases) are differentially reported with a given drug, as compared to other AEs (noncases, namely FAERS reports without AI) (7).”
From The Changing Face of Drug-induced Adrenal Insufficiency in the Food and Drug Administration Adverse Event Reporting System (2022) · cites this paper as [7]
The added value of pharmacovigilance studies has already been demonstrated in various settings.
“the added value of pharmacovigilance studies has already been demonstrated in various settings (3, 4, 7, 8).”
From The Changing Face of Drug-induced Adrenal Insufficiency in the Food and Drug Administration Adverse Event Reporting System (2022) · cites this paper as [7]
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Conducting and interpreting disproportionality analyses derived from spontaneous reporting systems
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- Authors
- Faillie, J.-L.
- Affiliations
- Université de Montpellier (FR)
- Corresponding author
- Faillie, Jean-Luc
- ORCID
- Faillie, Jean-Luc
- Venue
- Therapies
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 74
- Issue
- 2
- Pages
- 225-232
- ISSN
- 0040-5957 (ISSN-L)
- OpenAlex
- W3022784957
- Cited by
- 336
- References
- 2
- Citations by year
- 2026: 46 · 2025: 55 · 2024: 53 · 2023: 55 · 2022: 40 · 2021: 33 · 2020: 32 · 2019: 14 · 2018: 8
- FWCI
- 22.10
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Therapies (acceptedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by-nc)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Keywords
- adverse drug reactions, drug safety, pharmacovigilance, reporting odds ratio, disproportionality analysis, drug safety signals, confidence intervals, study bias, signal interpretation
- MeSH terms
- Data Interpretation, Statistical, Humans, Research Design, Bias, Confidence Intervals, Odds Ratio, Databases, Factual, Adverse Drug Reaction Reporting Systems, Pharmacovigilance, Drug-Related Side Effects and Adverse Reactions
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