ReviewPharmaceutical medicine2025
Disproportionality Analysis and Causal Inference in Drug Safety.
Review in Pharmaceutical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
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.
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Who cites it
13 citing papers in PubMed.
- Beyond Molecular Structures: Investigating Demographic Factors in Drug-Induced Cardiotoxicity Prediction Models.Journal of chemical information and modeling · 2026Article
- Post-marketing safety surveillance and adverse event risk factors of abemaciclib: a pharmacoepidemiologic study.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Causal Inference Tools for Pharmacovigilance: Using Causal Graphs to Identify and Address Biases in Disproportionality Analysis.Drug safety · 2026Article
- Charting and Sidestepping the Pitfalls of Disproportionality Analysis.Drug safety · 2026Review
- Indications and Adverse Events of Toradol: Based on FDA Adverse Event Reporting System (FAERS).Pain research & management · 2026Article
- Post-marketing safety of tarlatamab in small cell lung cancer based on FAERS and WHO-VigiAccess with SHAP-based interpretable machine learning analysis of immune-related adverse events.Frontiers in pharmacology · 2026Article
- Neurological and hematological safety profiles of GSK-3β inhibitors: insights from multi-database pharmacovigilance and experimental validation.Frontiers in immunology · 2026Article
- Pharmacovigilance analysis of infliximab in inflammatory bowel disease: novel safety signals and sex-specific adverse event profiles from the FAERS database (2004-2024).Frontiers in immunology · 2026Article
- Comparative post-marketing reporting signals of elagolix and myfembree in endometriosis: a FAERS pharmacovigilance study.Frontiers in pharmacology · 2026Article
- Neurological adverse events associated with baclofen: a pharmacovigilance study based on FDA adverse event reporting system.Frontiers in pharmacology · 2025Article
- PD-1/PD-L1 inhibitor-induced hyponatremia: a real-world pharmacovigilance analysis using FAERS database.Frontiers in immunology · 2025Article
- Mining and evaluation of security alert signals of neuroprotective agents based on the Jinan adverse event reporting system database: a retrospective study.Frontiers in pharmacology · 2025Article
- Remimazolam's clinical application and safety: A signal detection analysis based on FAERS data and literature support.PloS one · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Disproportionality analysis is a method of safety signal detection based on quantitative analysis of spontaneous reports of adverse events. Disproportionality findings are often presented in medical publications as real-world evidence on drug safety. In this paper, we review theoretical properties of disproportionality analysis in the framework of causal inference theory. We show that measures of disproportionality can approximate the causal rate ratio for a specific drug-event combination when the study drug and the set of comparator drugs satisfy all of the following conditions: (1) there is no uncontrolled confounding for the drug-event association of interest, (2) under-reporting for the event of interest is either absent or has the same relative magnitude for the study drug and for the comparator drugs, and (3) reporting rates for all adverse events combined are the same for the study drug and for the comparator drug set. Because these conditions are typically not even approximately satisfied in practice, the overwhelming majority of disproportionality hits represent statistical noise rather than causal associations. Researchers choosing to report disproportionality findings in publications should explicitly acknowledge all key assumptions and the exploratory nature of this data-mining technique.
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Registered trials
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.