ArticleDrug safety2017
Causality Assessment in Pharmacovigilance: Still a Challenge.
Article in Drug safety, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis 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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- When does Follow-up on Adverse Events Improve the Understanding of Medicine and Vaccine Safety? Insights from Global Expert Perspectives.Drug safety · 2026Article
- Do consumers and healthcare professionals report the same adverse event differently? A paired analysis of duplicate vaccine safety reports in Norway.British journal of clinical pharmacology · 2026Article
- Reporting patterns of adverse drug withdrawal events using individual case safety reports in the FAERS and Eudravigilance databases.Exploratory research in clinical and social pharmacy · 2026Article
- A real-world signal detection analysis uncovering the safety profile of ketorolac tromethamine.Medicine · 2026Observational
- Artificial intelligence in pharmacovigilance: a narrative review and practical experience with an expert-defined Bayesian network tool.International journal of clinical pharmacy · 2025Review
- Trust but Verify: Lessons Learned for the Application of AI to Case-Based Clinical Decision-Making From Postmarketing Drug Safety Assessment at the US Food and Drug Administration.Journal of medical Internet research · 2024Article
- Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study.Drug safety · 2023Article
- Indapamide-Induced Rhabdomyolysis: An Evaluation of Case Reports in VigiBase Using the Bradford Hill Criteria.Drugs - real world outcomes · 2022Article
- Leveraging Machine Learning to Facilitate Individual Case Causality Assessment of Adverse Drug Reactions.Drug safety · 2022Article
- Influence of Jamaican Cultural and Religious Beliefs on Adherence to Pharmacotherapy for Non-Communicable Diseases: A Pharmacovigilance Perspective.Frontiers in pharmacology · 2022Article
- Causality assessment between reported fatal cerebral haemorrhage and suspected drugs: developing a new algorithm based on the analysis of the Japanese Adverse Event Report (JADER) database and literature review.European journal of clinical pharmacology · 2021Review
- Countering Hesitancy and Misinformation on Side Effects to Complete the Course of COVID Vaccination.Journal of patient experience · 2021Article
- 'Engagement' of patients and healthcare professionals in regulatory pharmacovigilance: establishing a conceptual and methodological framework.European journal of clinical pharmacology · 2019Review
- Drug-Induced Anaphylaxis in a Vietnamese Pharmacovigilance Database: Trends and Specific Signals from a Disproportionality Analysis.Drug safety · 2019Article
- Vitamin BDrug safety · 2018Review
- Artificial intelligence in pharmacovigilance - Opportunities and challenges.Perspectives in clinical researchReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Causality in pharmacovigilance is a difficult and time consuming exercise. This paper presents the challenges in determining causation by drug therapy. The first is that causation is complex and needs to be viewed from the context of the patient treated, rather than the drug product. Multiple causal vectors should be considered if we are to tackle the many issues involved in, for example, medication error and the many other factors that lead to bad outcomes from therapy, including failure to recognise known risk factors. The aim of pharmacovigilance is not only a bureaucratic exercise in public health norms, but is mainly concerned with small minorities of statistical outliers-and even individuals-whose experiences from harms may together form messages about causation that will prevent further at-risk patients from exposure, or at least assist with earlier recognition of drug-related harm and better management of such harm. This requires more time, more data, more analysis and more patient and clinical involvement in reporting useful clinical detail. The paradigm shift back towards gathering more case data relating to possible causation can be selective and would not be just retrogressive, nor necessarily too costly. Greater transparency of hypotheses and availability of anonymised case data will enrol more expertise into evaluations and hypothesis testing, and the provision of more complete and useful information should reduce clinical burdens from bad patient outcomes as well as their overall costs to society.
Indexed as
Identifiers
28247280What 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.