ArticleDrug safety2014
Use of logistic regression to combine two causality criteria for signal detection in vaccine spontaneous report data.
Article in Drug safety, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Theoretical Framework and Key Considerations for Time-to-Onset Analysis in Spontaneous Reporting Systems.Drug safety · 2026Review
- Charting and Sidestepping the Pitfalls of Disproportionality Analysis.Drug safety · 2026Review
- Cardiovascular toxicity risk assessment of tyrosine kinase inhibitors: a pharmacovigilance study using the VigiBase database.Frontiers in pharmacology · 2024Article
- Evaluation of four machine learning models for signal detection.Therapeutic advances in drug safety · 2023Article
- Post-Marketing Safety Surveillance for the Adjuvanted Recombinant Zoster Vaccine: Methodology.Drug safety · 2020Article
- Time to onset in statistical signal detection revisited: A follow-up study in long-term onset adverse drug reactions.Pharmacoepidemiology and drug safety · 2019Article
- A prediction model-based algorithm for computer-assisted database screening of adverse drug reactions in the Netherlands.Pharmacoepidemiology and drug safety · 2018Article
- vigiRank for statistical signal detection in pharmacovigilance: First results from prospective real-world use.Pharmacoepidemiology and drug safety · 2017Article
- Pharmacoepidemiological considerations in observed-to-expected analyses for vaccines.Pharmacoepidemiology and drug safety · 2016Review
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe evaluated the use of logistic regression to model the probabilities of spontaneously reported vaccine-event pairs being adverse reactions following immunization (ARFI), using disproportionality and unexpectedness of time-to-onset (TTO) distributions as predictive variables and the presence of events in the global product information as a dependent variable.
methodsWe used spontaneous reports of adverse events from eight vaccines and their labels as proxies for ARFIs. Three logistic regressions were built to predict ARFIs based on different combinations of the proportional reporting ratio (PRR; disproportionality measure) and two Kolmogorov-Smirnov (KS) tests ('between vaccines' and the 'between events') of TTO distribution: model 1, using the PRR estimate and its 95% lower confidence interval (CI) limit; model 2, using the p values of the two KS tests; and model 3, using the PRR (point estimate and lower CI limit) and both KS tests. The performance of the regressions (model fit statistics, calibration, and discrimination) was measured on 100 bootstrap samples.
resultsModel 3, using two quantified causality criteria, provided the best performance for all measures. The p value of the 'between vaccines' KS test was the most significant predictive factor. Model 1 had the worst performance.
conclusionsLogistic regression allows estimation of the probability of a vaccine-event pair being an ARFI using two causality criteria at the population level assessed in spontaneous report data: the strength of association (disproportionality measure) and temporality (TTO distribution tests). Logistic regression combines and weights these causality criteria based on their respective ability to predict known safety issues.
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