Evidence map›Paper›PMID 39987376›Full record

ArticleDrug safety2025

Are Causal Statements Reported in Pharmacovigilance Disproportionality Analyses Using Individual Case Safety Reports Exaggerated in Related Citations? A Meta-epidemiological Study.

Claire Bernardeau, Bruno Revol, Francesco Salvo, Michele Fusaroli, Emanuel Raschi, Jean-Luc Cracowski, Matthieu Roustit, Charles Khouri

Abstract read
In one paragraph

Article in Drug safety, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Disproportionality analysis to investigate fatal adverse events with immune checkpoint inhibitors: Proceed with extreme caution.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Claire BernardeauPharmacovigilance Unit, Grenoble Alpes University Hospital, University Grenoble Alpes, 38000, Grenoble, France.
Bruno RevolPharmacovigilance Unit, Grenoble Alpes University Hospital, University Grenoble Alpes, 38000, Grenoble, France.
Francesco SalvoUniversité de Bordeaux, INSERM, BPH, Team AHeaD, U1219, 33000, Bordeaux, France.
Michele FusaroliDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy.
Emanuel RaschiDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy.
Jean-Luc CracowskiPharmacovigilance Unit, Grenoble Alpes University Hospital, University Grenoble Alpes, 38000, Grenoble, France.
Matthieu RoustitUniversity Grenoble Alpes, Inserm U1300, HP2, Grenoble, France.
Charles KhouriPharmacovigilance Unit, Grenoble Alpes University Hospital, University Grenoble Alpes, 38000, Grenoble, France. CKhouri@chu-grenoble.fr.ORCID http://orcid.org/0000-0002-8427-8573

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevious meta-epidemiological surveys have found considerable misinterpretation of results of disproportionality analyses. We aim to explore the relationship between the strength of causal statements used in title and abstract conclusions of pharmacovigilance disproportionality analyses and the strength of causal language used in citing studies.

methodsOn March 30, 2022, we selected the 30 disproportionality studies with the highest Altmetric Attention Scores. For each article, we extracted all citing studies using the Dimension database (n = 1434). In parallel, two authors assessed the strength of causal statements in the title and abstract conclusions of source articles and in the paragraph of citing studies. Based on previous studies, the strength of causal language was quantified based on a four-level scale (1-appropriate interpretation; 2-ambiguous interpretation; 3-conditionally causal; 4-unconditionally causal). Discrepancies were solved by discussion until consensus among the team. We assessed the association between the strength of causal statements in source articles and citing studies, separately for the title and abstract conclusions, through multinomial regression models.

resultsOverall, 27% (n = 8) of source studies used unconditionally causal statements in their title, 30% (n = 9) in their abstract conclusion, and 17% (n = 5) in both. Only 20% (n = 6) used appropriate statements in their title and in their abstract's conclusions. Among the 622 citing studies analyzed, 285 (45.8%) used unconditionally causal statements when referring to the findings from disproportionality analysis, and only 164 (26.4%) used appropriate language. Multinomial models found that the strength of causal statements in citing studies was positively associated with the strength of causal language used in abstract conclusions of source articles (Likelihood Ratio Test (LogLRT) p < 0.00001) but not in the titles. In particular, among studies citing source articles with appropriate interpretation, 30.2% (95% confidence interval [CI] 22.8-37.6) contained unconditionally causal statements in their abstract conclusions, versus 56.4% (95% CI 48.7-64.2) for studies citing source articles with unconditionally causal statements.

conclusionsNearly half of the studies citing pharmacovigilance disproportionality analyses results used causal claims, particularly when the causal language used in the source article was stronger. There is a need for higher caution when writing, interpreting, and citing disproportionality studies.

Indexed as

Adverse Drug Reaction Reporting SystemsDrug-Related Side Effects and Adverse ReactionsPharmacovigilanceCausalityEpidemiologic StudiesHumans

Identifiers

PMID39987376
PMCPMC12098493

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.