ReviewClinical drug investigation2017
Can Disproportionality Analysis of Post-marketing Case Reports be Used for Comparison of Drug Safety Profiles?
Review in Clinical drug investigation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
51 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Safety of sulfamethoxazole-trimethoprim for the treatment of bacterial infection in outpatient settings: A systematic review and meta-analysis with active comparator disproportionality analysis.British journal of clinical pharmacology · 2025Pooled it
- Safety of sulfamethoxazole-trimethoprim for the treatment of bacterial infection in outpatient settings: A systematic review and meta-analysis with active comparator disproportionality analysis.British journal of clinical pharmacology · 2025Pooled it
- Article
- Cardiovascular Safety Signals of Osteoporosis Therapies: A FAERS Pharmacovigilance Study.Mediterranean journal of rheumatology · 2026Article
- Disproportionality analysis and "perpetrator" or "accomplice" review of Baloxavir marboxil in public FAERS database.BMC infectious diseases · 2026Article
- Hemorrhagic risk of concomitant direct oral anticoagulants and fluoroquinolones: integration of pharmacovigilance and therapeutic drug monitoring.Frontiers in pharmacology · 2026Article
- Psychiatric events induced by roflumilast: a real-world pharmacovigilance study of the FDA Adverse Event Reporting System database.Frontiers in psychiatry · 2026Article
- Real-world safety profile of givinostat: an early post-marketing pharmacovigilance study based on the FAERS database.Frontiers in pharmacology · 2026Article
- Comparative post-marketing reporting signals of elagolix and myfembree in endometriosis: a FAERS pharmacovigilance study.Frontiers in pharmacology · 2026Article
- Drug-drug interactions between sex hormones and analgesics linked to thromboembolic events: an FDA adverse event reporting system analysis.Frontiers in pharmacology · 2026Article
- Bridging pharmacovigilance and genetic insight: investigating drugs and indications for breast cancer risk in women with autoimmune diseases.Journal of translational medicine · 2025Article
- A Real-World Pharmacovigilance Study of Fruquintinib Based on the FDA Adverse Event Reporting System (FAERS) Database.Cancer medicine · 2025Article
- Disproportionality analysis of drug-induced erectile dysfunction using FAERS database.Scientific reports · 2025Article
- Disproportionality Analysis and Causal Inference in Drug Safety.Pharmaceutical medicine · 2025Review
- Adverse events of pexidartinib for the treatment of TGCT: a real-world disproportionality analysis using FDA Adverse Event Reporting System database.Frontiers in oncology · 2025Article
- Commentary: Musculoskeletal adverse events in dogs receiving bedinvetmab (Librela).Frontiers in veterinary science · 2025Article
- Comprehensive safety assessment of donepezil: pharmacovigilance analysis based on the FDA adverse event reporting system.Frontiers in neurology · 2025Article
- A real-world pharmacovigilance study of FDA adverse event reporting system events for Lutetium-177-PSMA-617.Scientific reports · 2024Article
- A real-world disproportionality analysis of FDA adverse event reporting system (FAERS) events for alpelisib.Heliyon · 2024Article
- New Adverse Drug Reaction Signals from 2017 to 2021-Genuine Alerts or False Alarms?Pharmacy (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical trials usually do not have the power to detect rare adverse drug reactions. Spontaneous adverse reaction reports as for example available in post-marketing safety databases such as the FDA Adverse Event Reporting System (FAERS) are therefore a valuable source of information to detect new safety signals early. To screen such large data-volumes for safety signals, data-mining algorithms based on the concept of disproportionality have been developed. Because disproportionality analysis is based on spontaneous reports submitted for a large number of drugs and adverse event types, one might consider using these data to compare safety profiles across drugs. In fact, recent publications have promoted this practice, claiming to provide guidance on treatment decisions to healthcare decision makers. In this article we investigate the validity of this approach. We argue that disproportionality cannot be used for comparative drug safety analysis beyond basic hypothesis generation because measures of disproportionality are: (1) missing the incidence denominators, (2) subject to severe reporting bias, and (3) not adjusted for confounding. Hypotheses generated by disproportionality analyses must be investigated by more robust methods before they can be allowed to influence clinical decisions.
Indexed as
Identifiers
28224371What OpenQuestion holds
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.