Conducting and interpreting disproportionality analyses derived from spontaneous reporting systems
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as a standalone approach to assess a drug-related risk and cannot replace clinical judgment in the individual patient, their role remain irreplaceable for rapid detection of rare and unpredictable adverse drug reactions with strong drug-attributable component (e.g., designated medical events), especially when developed by a multidisciplinary team and combined with a careful case-by-case analysis (individual inspection of reports for causality assessment or to uncover reporting patterns and clinical features). In the recent past, a remarkable increase in publications of pharmacovigilance studies using DAs was observed, albeit the quality was debated: several publications contained "spin", namely, misinterpretation of results to infer causality, calculate incidence, or provide risk stratification, which may ultimately result in unjustified alarm. The development of dedicated Guidelines by the international READUS-PV project (https://readus-statement.org/) will allow reproducible and transparent publication of accurate DAs, thus supporting their real transferability and exploitation by regulators and clinicians. This review offered a perspective on methodological aspects (and understanding) of DAs, their rationale, design, reporting, and interpretation.
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Disproportionality analyses are only measures of drug safety signals and cannot be considered indicators of association or causality.
“Lastly, the disproportionality analyses are only measures of drug safety signals and cannot be considered indicators of association or causality (27).”
From Risk of Death and Adverse Effects in Patients on Liothyronine: A Multisource Systematic Review and Meta-analysis (2025) · cites this paper as [27]
Section 3 describes disproportionality analysis as a first-pass method for detecting safety signals, and Section 6 says causality cannot be inferred from its results. However, Section 2 defines a signal as suggesting a potentially causal association. The claim overstates the source by saying the analyses cannot be indicators of association at all, rather than saying they cannot establish one. (LLM, conf=0.82)
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References 133 · Cited by 224
- Authors
- Cutroneo, P. M., Sartori, D., Tuccori, M., Crisafulli, S., Battini, V., Carnovale, C., Rafaniello, C., Capuano, A., Poluzzi, E., Moretti, U., & Raschi, E.
- Affiliations
- University of Messina (IT)
- Uppsala Monitoring Centre (SE)
- University of Pisa (IT)
- University of Verona (IT)
- University of Milan (IT)
- University of Campania "Luigi Vanvitelli" (IT)
- University of Bologna (IT)
- Corresponding author
- Tuccori, Marco
- ORCID
- Venue
- Frontiers in Drug Safety and Regulation
- Publisher
- Frontiers Media SA
- Published
- 2024-01-26
- Volume
- 3
- Pages
- 1323057-1323057
- ISSN
- 2674-0869 (ISSN-L)
- PMC
- PMC12443087
- OpenAlex
- W4391261634
- Cited by
- 224
- References
- 133
- Citations by year
- 2026: 125 · 2025: 71 · 2024: 28
- FWCI
- 15.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Drug Safety and Regulation (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Archivio Istituzionale della Ricerca - Università degli Studi della Campania "Luigi Vanvitelli" (submittedVersion)
- Funders
- World Health Organization
- 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
- disproportionality analysis, spontaneous reporting systems, adverse drug reactions, pharmacovigilance, drug safety, causality assessment, risk stratification, publication quality, drug-related risks
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