ArticlePharmaceutics2013
Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic.
Article in Pharmaceutics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methods for drug safety signal detection using routinely collected observational electronic health care data: A systematic review.Pharmacoepidemiology and drug safety · 2023Pooled it
- Article
- Congenital malformation risk following prenatal antipsychotic exposure: a systematic safety surveillance approach.BMJ mental health · 2026Article
- Adverse drug-event detection using the tree-based scan statistics (TreeScan) and comparison with common mining methods: new user, propensity score-matched cohorts.European journal of clinical pharmacology · 2026Article
- Hierarchical clustering analysis to inform classification of congenital malformations for surveillance of medication safety in pregnancy.American journal of epidemiology · 2025Article
- An open-source implementation of tree-based scan statistics.Pharmacoepidemiology and drug safety · 2024Article
- Signal detection of adverse events associated with gabapentinoid use for chronic pain.Pharmacoepidemiology and drug safety · 2024Article
- A New Drug Safety Signal Detection and Triage System Integrating Sequence Symmetry Analysis and Tree-Based Scan Statistics with Longitudinal Data.Clinical epidemiology · 2023Article
- OAE-based data mining and modeling analysis of adverse events associated with three licensed HPV vaccines.Heliyon · 2022Article
- Signal Detection of Adverse Events Associated with Trastuzumab in a Cohort of Elderly Patients with Breast Cancer.The oncologist · 2022Article
- A Broad Safety Assessment of the Recombinant Herpes Zoster Vaccine.American journal of epidemiology · 2022Article
- A novel data mining application to detect safety signals for newly approved medications in routine care of patients with diabetes.Endocrinology, diabetes & metabolism · 2021Article
- A Broad Safety Assessment of the 9-Valent Human Papillomavirus Vaccine.American journal of epidemiology · 2021Article
- Comparison of Data Mining Methods for the Signal Detection of Adverse Drug Events with a Hierarchical Structure in Postmarketing Surveillance.Life (Basel, Switzerland) · 2020Article
- An Implementation and Visualization of the Tree-Based Scan Statistic for Safety Event Monitoring in Longitudinal Electronic Health Data.Drug safety · 2019Article
- Hypothesis-free signal detection in healthcare databases: finding its value for pharmacovigilance.Therapeutic advances in drug safety · 2019Article
- Data Mining for Adverse Drug Events With a Propensity Score-matched Tree-based Scan Statistic.Epidemiology (Cambridge, Mass.) · 2018Article
- Assessment of Quadrivalent Human Papillomavirus Vaccine Safety Using the Self-Controlled Tree-Temporal Scan Statistic Signal-Detection Method in the Sentinel System.American journal of epidemiology · 2018Article
- Statistical Power for Postlicensure Medical Product Safety Data Mining.EGEMS (Washington, DC) · 2017Article
- Sequence Symmetry Analysis as a Signal Detection Tool for Potential Heart Failure Adverse Events in an Administrative Claims Database.Drug safety · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
Abstract
backgroundDrug adverse event (AE) signal detection using the Gamma Poisson Shrinker (GPS) is commonly applied in spontaneous reporting. AE signal detection using large observational health plan databases can expand medication safety surveillance.
methodsUsing data from nine health plans, we conducted a pilot study to evaluate the implementation and findings of the GPS approach for two antifungal drugs, terbinafine and itraconazole, and two diabetes drugs, pioglitazone and rosiglitazone. We evaluated 1676 diagnosis codes grouped into 183 different clinical concepts and four levels of granularity. Several signaling thresholds were assessed. GPS results were compared to findings from a companion study using the identical analytic dataset but an alternative statistical method-the tree-based scan statistic (TreeScan).
resultsWe identified 71 statistical signals across two signaling thresholds and two methods, including closely-related signals of overlapping diagnosis definitions. Initial review found that most signals represented known adverse drug reactions or confounding. About 31% of signals met the highest signaling threshold.
conclusionsThe GPS method was successfully applied to observational health plan data in a distributed data environment as a drug safety data mining method. There was substantial concordance between the GPS and TreeScan approaches. Key method implementation decisions relate to defining exposures and outcomes and informed choice of signaling thresholds.
Identifiers
What 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.