Cited by 338Statistical Methods in EpidemiologyStatistical Methods in Clinical TrialsAdvanced Causal Inference Techniques

The DOI matches its Crossref record, with a small difference in title or year; 26 of the 26 references it cites were found in a registry or an index.

Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery

Open access

Norén, G Niklas, Hopstadius, Johan, Bate, Andrew·2013·Statistical Methods in Medical Research

DOI 10.1177/0962280211403604PMC PMC6331976

Open this paper in CoChat — free account Open PDF

Abstract

Large observational data sets are a great asset to better understand the effects of medicines in clinical practice and, ultimately, improve patient care. For an empirical pattern in observational data to be of practical relevance, it should represent a substantial deviation from the null model. For the purpose of identifying such deviations, statistical significance tests are inadequate, as they do not on their own distinguish the magnitude of an effect from its data support. The observed-to-expected (OE) ratio on the other hand directly measures strength of association and is an intuitive basis to identify a range of patterns related to event rates, including pairwise associations, higher order interactions and temporal associations between events over time. It is sensitive to random fluctuations for rare events with low expected counts but statistical shrinkage can protect against spurious associations. Shrinkage OE ratios provide a simple but powerful framework for large-scale pattern discovery. In this article, we outline a range of patterns that are naturally viewed in terms of OE ratios and propose a straightforward and effective statistical shrinkage transformation that can be applied to any such ratio. The proposed approach retains emphasis on the practical relevance and transparency of highlighted patterns, while protecting against spurious associations.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1177/0962280211403604

Cites

26 of 26 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Estimation with Quadratic Loss
James, W., & Stein, C. M. (1992) · Springer series in statistics · doi:10.1007/978-1-4612-0919-5_30 · Cited by 2,319
Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports
Evans, S., WALLER, P. C., & Davis, S. (2001) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.677 · Cited by 1,808
A Bayesian neural network method for adverse drug reaction signal generation
Bate, A., Lindquist, M., Edwards, I. R., Olsson, S., Orre, R., Lansner, A., & Freitas, R. M. D. (1998) · European Journal of Clinical Pharmacology · doi:10.1007/s002280050466 · Cited by 1,492
Self-citation
Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System
DuMouchel, W. (1999) · The American Statistician · doi:10.1080/00031305.1999.10474456 · Cited by 883
Selecting the right interestingness measure for association patterns
Tan, P., Kumar, V., & Srivastava, J. (2002) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/775047.775053 · Cited by 868
CONCEPTS OF INTERACTION
ROTHMAN, K. J., GREENLAND, S., & WALKER, A. M. (1980) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a113015 · Cited by 859 · cited 1×
“The additive baseline model has clear advantages as a basis for both individual decision-making and public policy-making.(13) Moreover, empirical results suggest that it is better suited to detect some patterns indicative of adverse drug interactions, at least in individual case safety reports.”
Background
Large-Scale Bayesian Logistic Regression for Text Categorization
Genkin, A., Lewis, D. D., & Madigan, D. (2007) · Technometrics · doi:10.1198/004017007000000245 · Cited by 812
VigiBase, the WHO Global ICSR Database System: Basic Facts
Lindquist, M. (2008) · Drug Information Journal · doi:10.1177/009286150804200501 · Cited by 599
Selective serotonin reuptake inhibitors in pregnant women and neonatal withdrawal syndrome: a database analysis
Sanz, E. J., Cuevas, C. D. L., Kiuru, A., Bate, A., & Edwards, R. (2005) · The Lancet · doi:10.1016/s0140-6736(05)17865-9 · Cited by 379
Self-citation
Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability:
F., D. D., & Neyman, J. (1957) · Journal of the American Statistical Association · doi:10.2307/2280857 · Cited by 351

Flagged first, then by citations

Show all 26 references

Sorted by citations

Items 201–300 of 338

Contribution of Causality Assessment for an Automated Detection of Safety Signals: An Example Using the French Pharmacovigilance Database
Berbain, T., Pariente, A., Miremont‐Salamé, G., Grandvuillemin, A., Micallef, J., Chouchana, L., Benkebil, M., & Théophile, H. (2020) · Drug Safety · doi:10.1007/s40264-019-00887-2 · Cited by 6
Biases associated with epidermal necrolysis reporting in pharmacovigilance: An exploratory analysis using World Health Organization VigiBase
Bettuzzi, T., Ingen‐Housz‐Oro, S., Maison, P., de Prost, N., Wolkenstein, P., Lebrun‐Vignes, B., & Sbidian, É. (2021) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.5399 · Cited by 6
Drug-induced Stevens-Johnson syndrome: a disproportionality analysis from the pharmacovigilance database of the World Health Organization
Zheng, Y., Zhou, W., Guo, X., Chi, L., Chen, C., Guo, Z., Liang, J., Wei, L., Chen, X., Ye, X., et al. (2022) · Expert Opinion on Drug Safety · doi:10.1080/14740338.2022.2045946 · Cited by 6
Hydrochlorothiazide and risk of hearing disorder: a case series
Belai, N., Gebrehiwet, S., Fitsum, Y., & Russom, M. (2018) · Journal of Medical Case Reports · doi:10.1186/s13256-018-1580-8 · Cited by 6
Computational pharmacovigilance of Lifitegrast in dry eye disease using machine learning and network toxicology
Li, L., Jing, S., Zhao, X., Zhu, X., Liang, C., Shi, L., Zhou, P., Xie, K., Jin, B. R., Zhu, H., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02173-1 · Cited by 5
Dysesthesia associated with GLP-1 agonist therapies: data-mining analysis and literature review
Laroche, M., Geniaux, H., & Jardou, M. (2026) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-026-04079-7 · Cited by 5
Navigating the complex landscape of benzodiazepine- and Z-drug diversity: insights from comprehensive FDA adverse event reporting system analysis and beyond
Koniuszewski, F., Vogel, F. D., Dajic, I., Seidel, T., Kunze, M., Willeit, M., & Ernst, M. C. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1188101 · Cited by 6
Network Analysis and Machine Learning for Signal Detection and Prioritization Using Electronic Healthcare Records and Administrative Databases: A Proof of Concept in Drug-Induced Acute Myocardial Infarction
Barbieri, M. A., Abate, A., Balogh, O. M., Pétervári, M., Ferdinandy, P., Ágg, B., Battini, V., Cocco, M., Rossi, A., Carnovale, C., et al. (2025) · Drug Safety · doi:10.1007/s40264-025-01515-y · Cited by 5
Exploring the potential association between stimulant or atomoxetine use and suicidal or self-injurious behaviors in children with attention deficit hyperactivity disorder: real-world insights from the FAERS database
Liu, J., Xue, L., Zeng, F., Ying, L., Zhu, Y., Zhou, J., Zhang, J., & Chen, H. (2025) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-025-02694-w · Cited by 5
Drug-induced delusion: A comprehensive overview of the WHO pharmacovigilance database
Balcerac, A., Baldacci, A., Romier, A., Annette, S., Lemarchand, B., Bihan, K., & Bottemanne, H. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115365 · Cited by 5
Long-term hepatobiliary disorder associated with trastuzumab emtansine pharmacovigilance study using the FDA Adverse Event Reporting System database
Kim, H. J., Yoon, J.-H., & Park, Y. H. (2024) · Scientific Reports · doi:10.1038/s41598-024-69614-x · Cited by 5
Signal detection of drugs associated with obstructive and central sleep apnoea
Jambon-Barbara, C., Revol, B., Hlavaty, A., Joyeux‐Faure, M., Borel, J., Cracowski, J., Pépin, J., & Khouri, C. (2024) · Sleep Medicine · doi:10.1016/j.sleep.2024.09.045 · Cited by 5
Signals of Adverse Reactions to Herbal Medicines: Evidence and Document Analysis Based on a Scoping Review
Sartori, D., Aronson, J. K., Brand, J. S., Gauffin, O., Vidlin, S. H., Norén, G. N., & Onakpoya, I. (2025) · Drug Safety · doi:10.1007/s40264-025-01580-3 · Cited by 5
Self-citation
An information theory account of preference prediction accuracy
Pollmann, M. M. H., & Scheibehenne, B. (2014) · Journal of Consumer Psychology · doi:10.1016/j.jcps.2014.10.002 · Cited by 5
Metabolic and Nutritional Disorders Following the Administration of Immune Checkpoint Inhibitors: A Pharmacovigilance Study
Zhai, Y., Ye, X., Hu, F., Xu, J., Guo, X., Zhou, X., Zheng, Y., Zhao, X., Xu, X. G., Cao, Y., et al. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.809063 · Cited by 5
HIF-PHIs associated with embolic and thrombotic events: a real-world pharmacovigilance study based on the Japan Adverse Drug Event Report database
Wu, D., Jiao, S., Lin, H., Xie, P., Cai, G., & Lin, M. (2025) · Renal Failure · doi:10.1080/0886022x.2025.2491655 · Cited by 4
Pharmacovigilance study for the identification of mogamulizumab-induced immune-related adverse events using a real-world database
Miyata, K., Izawa‐Ishizawa, Y., Niimura, T., Yoshioka, T., Hyodo, M., Itokazu, S., Miyata, T., Aizawa, F., Yagi, K., Kawada, K., et al. (2025) · The Oncologist · doi:10.1093/oncolo/oyaf201 · Cited by 4
Drug-induced immune-mediated uveitis: a real-world pharmacovigilance analysis using FAERS and JADER databases
Xu, S., Song, Z., Li, Y. H., Bai, J., Wang, D., Wang, E., & Wang, J. (2025) · BMJ Open Ophthalmology · doi:10.1136/bmjophth-2025-002456 · Cited by 4
Exploring the Link Between Exogenous Thyroid Hormones and Dementia Symptoms: A Real-World Disproportionality Analysis of FDA Adverse Event Reporting System
Zhou, J., Lai, W., Wei, Z., Xu, B., Liu, M., Zhang, N., & Wu, X. (2024) · Annals of Pharmacotherapy · doi:10.1177/10600280241252211 · Cited by 4
Cardiovascular toxicities of selective ret-specific tyrosine kinase inhibitors: a pharmacovigilance study based on the United States Food and Drug Administration Adverse Event Reporting System database
Hou, Y., Ren, X., Chen, Y. S., & Wang, Y. (2024) · Expert Opinion on Drug Safety · doi:10.1080/14740338.2024.2392003 · Cited by 4
Exploration of the potential association between newer antiseizure medications and arrhythmias: Integrating pharmacovigilance and bioinformatics evidence
Zhou, J., Chen, Z., Zhang, M., Ye, Y., Shen, Y., & Wu, X. (2024) · Seizure · doi:10.1016/j.seizure.2024.10.011 · Cited by 4
Amiodarone and Parkinsonism: a pharmacovigilance study
Montastruc, J., & Durrieu, G. (2020) · Fundamental and Clinical Pharmacology · doi:10.1111/fcp.12618 · Cited by 4
Characteristics and outcomes of gemcitabine-associated pulmonary hypertension
Mouillot, P., Favrolt, N., Khouri, C., Grandvuillemin, A., Chaumais, M., Schenesse, D., Seferian, A., Jaïs, X., Savale, L., Beltramo, G., et al. (2024) · ERJ Open Research · doi:10.1183/23120541.00654-2023 · Cited by 4
Reply: Drugs That Interact With Colchicine Via Inhibition of Cytochrome P450 3A4 and P-Glycoprotein: A Signal Detection Analysis Using a Database of Spontaneously Reported Adverse Events (FAERS)
Malone, D. C., Gómez-Lumbreras, A., Boyce, R. D., Zapata, L. V., Tan, M. S., Hansten, P. D., & Horn, J. R. (2023) · Annals of Pharmacotherapy · doi:10.1177/10600280231168860 · Cited by 4
Peripheral Neuropathy in Patients With Ovarian Cancer Administrating Poly ADP-Ribose Polymerase Inhibitors: A Real-World Study Based on Bayesian Disproportionality Analysis of the US Food and Drug Administration Adverse Event Reporting System
Yang, C., Song, X., Sun, H., Chen, X., Liu, C., Yang, Y., Wang, Z., Zhu, J., & Chen, M. (2025) · Clinical Therapeutics · doi:10.1016/j.clinthera.2025.04.016 · Cited by 3
No disproportionate reporting of inflammatory arthritis following COVID-19 vaccination: a pharmacovigilance study using VAERS data
Ciaffi, J., Torrigiani, G., Ruscitti, P., Zambon, A., & Ursini, F. (2025) · Seminars in Arthritis and Rheumatism · doi:10.1016/j.semarthrit.2025.152827 · Cited by 3
Exploration of the potential association between new oral anticoagulants and anxiety symptoms adverse events: Evidence from a real-world analysis
Deng, Z., Yang, K., Han, X., Gong, H., Cai, X., Hu, L., Ni, K., Xiong, Y., & Gao, W. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.120355 · Cited by 3
Vonoprazan-related adverse events: a pharmacovigilance study based on the FDA adverse event reporting system
Zhong, Y., Hou, C., Zhang, J., Li, W., Xiong, D., Huang, Z. W., & Luo, X. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1688914 · Cited by 3
A pharmacovigilance data–driven approach to reveal high fatal adverse events following checkpoint immunotherapy
Sun, Z., Deng, X., Guo, J., Liu, M., Wang, S., Wang, J., Zheng, J., Niu, K., Shen, W., Ye, Z., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2427234123 · Cited by 3
Clinical Relatedness and Stability of vigiVec Semantic Vector Representations of Adverse Events and Drugs in Pharmacovigilance
Erlanson, N., China, J. F., Taavola, H., & Norén, G. N. (2025) · Drug Safety · doi:10.1007/s40264-024-01509-2 · Cited by 3
Self-citation
A real-world disproportionality analysis of ripretinib data mining of the public version of FDA adverse event reporting system
Feng, Y., Fa, X., Wang, Y., Zhang, T., Sun, X., & Li, F. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1469597 · Cited by 3
Linezolid-related Optic Nerve Disorders: Insight From a Pharmacovigilance Analysis of the US FDA Adverse Event Reporting System
Nakano, Y., Miyazaki, M., Fukuyama, A., Hasuwa, H., & Imakyure, O. (2025) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofaf520 · Cited by 3
Adverse effects of posaconazole on adrenal steroid biosynthesis: An integrative approach using FAERS-based pharmacovigilance and systematic review with meta-analysis of randomised controlled trials
Nakano, Y., Kotake, K., Asai, Y., Murata, M., Uchiyama, M., Shimono, N., & Hasuwa, H. (2025) · International Journal of Antimicrobial Agents · doi:10.1016/j.ijantimicag.2025.107676 · Cited by 3
Severe muscle symptoms with lipid-lowering agents may be confused with neurogenic claudication associated with spinal canal stenosis
Savage, R. L., Star, K., & Hill, R. L. (2012) · International Journal of Risk & Safety in Medicine · doi:10.3233/jrs-2012-0577 · Cited by 3
Safety of selinexor as the only exportin 1 (XPO1) inhibitor so far: a post-marketing study based on the world Health Organization pharmacovigilance database (Vigibase)
Chen, C., Miao, X., Guo, X., Xu, J., Liang, J., Zheng, Y., Chi, L., Chen, X., Wei, L., Zhang, H., et al. (2023) · Expert Opinion on Drug Safety · doi:10.1080/14740338.2023.2248888 · Cited by 3
Epidemiological surveillance of drug safety using cumulative sequential analysis in electronic healthcare data
Aakjær, M., Bruin, M. D. D., & Andersen, M. (2023) · Basic & Clinical Pharmacology & Toxicology · doi:10.1111/bcpt.13955 · Cited by 3
Hemorrhage profile associated with immune checkpoint inhibitors: a systematic review and a real-world study based on the FAERS database
Chen, M., He, Z., Zhu, J., Yang, S., Gao, S., Wu, J., Ren, H., Liang, D., Jiang, W., Zou, Y. K., et al. (2024) · Expert Opinion on Drug Safety · doi:10.1080/14740338.2024.2327504 · Cited by 3
Vigicaen: A vigibase® Pharmacovigilance Database Toolbox.
Dolladille, C., & Chrétien, B. (2025) · The Journal of Open Source Software · doi:10.21105/joss.10321 · Cited by 3
Indapamide-Induced Rhabdomyolysis: An Evaluation of Case Reports in VigiBase Using the Bradford Hill Criteria
Yue, Q., & Caduff‐Janosa, P. (2022) · Drugs - Real World Outcomes · doi:10.1007/s40801-022-00295-6 · Cited by 3
Gastrointestinal adverse events associated with Lenvatinib versus Lenvatinib plus Pembrolizumab: A pharmacovigilance study in FDA adverse event reporting system
Ding, C., Ma, L., Liang, Y., Zhang, Z., Wu, Q., Lyu, J., & Su, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-99773-4 · Cited by 2
Comprehensive safety assessment of ribociclib: A real‐world analysis using the FDA Adverse Event Reporting System (FAERS) database
Luo, L., Wang, Y., Fu, Y., Chen, X., Liu, S., & Zhao, B. (2025) · British Journal of Clinical Pharmacology · doi:10.1002/bcp.70265 · Cited by 2
Sex Differences in Cardiotoxicity of Anaplastic Lymphoma Kinase Inhibitors: An Analysis of the FDA Adverse Event Reporting System
Asano, H., Noguchi, Y., Masuda, R., Go, M., Kimura, M., Usami, E., & Yoshimura, T. (2025) · Oncology · doi:10.1159/000548896 · Cited by 2
Respiratory Disorders Associated with Antibody–Drug Conjugates: A Combined Analysis of the French and the WHO Pharmacovigilance Databases
Mirleau, V., Hlavaty, A., Jutant, E., Hennegrave, F., Bihan, K., Freppel, R., Cholle, C., Bourneau‐Martin, D., Lafay‐Chebassier, C., Mahé, J., et al. (2025) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.70119 · Cited by 2
Safety profile of metformin in adolescents with type 2 diabetes: A pharmacovigilance analysis of the FDA Adverse Event Reporting System
Peng, M., Shen, P., Joung, K.-I., & Kim, K. J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0337204 · Cited by 2
Peripheral Neuropathy Associated With Proteasome Inhibitors and Immunomodulatory Drugs: A Pharmacovigilance Disproportionality Analysis Using VigiBase
Braga, L. P., de Pádua, C. A. M., da Costa, I. H. F., Drummond, P. L. D. M., de Souza, P. H. C., Fonseca, L. B., Alacoque, M., Malta, J. S., & Reis, A. M. M. (2025) · Pharmacology Research & Perspectives · doi:10.1002/prp2.70191 · Cited by 2
Comparative Real-World Safety Profiles of Six Selective Serotonin Reuptake Inhibitors: A Global Pharmacovigilance Analysis
Chan, A. C. Y. (2025) · Cureus · doi:10.7759/cureus.98677 · Cited by 4
COVID-19 vaccination and systemic autoimmune rheumatic diseases: No evidence of disproportionately increased reporting in VAERS
Ciaffi, J., Torrigiani, G., Ruscitti, P., Zambon, A., & Ursini, F. (2026) · Seminars in Arthritis and Rheumatism · doi:10.1016/j.semarthrit.2025.152911 · Cited by 2
Real-World Evidence Evaluation of Respiratory Syncytial Virus (RSV) Vaccines: Deep Dive into Vaccine Adverse Events Reporting System
Alshammari, T. M., Alshammari, M. K., & Alosaimi, H. (2026) · Diseases · doi:10.3390/diseases14010029 · Cited by 2
Real-World Sodium Fluoride Safety Signals: Dual-Database Pharmacovigilance
Ning, W., Huang, X., Li, S., Jiang, L., Reissmann, D. R., Kreher, D., Schmalz, G., Chen, L., & Huang, S. (2026) · International Dental Journal · doi:10.1016/j.identj.2026.109423 · Cited by 2
Response to ‘The association of clozapine and haematological malignancies needs to be replicated by other studies and more importantly by analyses of subsamples from VigiBase’
Chrétien, B., Lelong‐Boulouard, V., Alexandre, J., Fédrizzi, S., & Dolladille, C. (2020) · Psychological Medicine · doi:10.1017/s0033291720002317 · Cited by 2
Post-marketing safety of finerenone: a disproportionality analysis of the FDA adverse event reporting system
Jin, Y., Fan, M., Zheng, X., & Zhu, S. (2024) · Expert Opinion on Drug Safety · doi:10.1080/14740338.2024.2392006 · Cited by 2
Neurotoxicity associated with chimeric antigen receptor T-cell therapy: a real-world study leveraging the FDA Adverse Event Reporting System
Zhai, Y., Yuan, L., Fang, S., Liu, S.-G., Ye, X., Shi, W., Cao, Y., He, J., Hu, F., & Feng, X. (2024) · Expert Opinion on Drug Safety · doi:10.1080/14740338.2024.2416542 · Cited by 2
Comprehensive disproportionality analysis of individual case safety reports associated with Janus kinase inhibitors in psoriasis and psoriatic arthritis using the FAERS database
Deng, K., Jiang, H., Xie, S., Yin, M., Guo, C., Huang, L., Ma, J., Kuang, Y., Xiang, Y., & Guo, C. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1629886 · Cited by 2
Drug‐Induced Raynaud's Phenomenon and Underlying Mechanism: A Disproportionality Analysis From the World Health Organization Pharmacovigilance Database
Hlavaty, A., Dari, L., Cracowski, J., Roustit, M., & Khouri, C. (2025) · Arthritis & Rheumatology · doi:10.1002/art.43442 · Cited by 2
Characterization of VigiBase reports on tinnitus associated with bisoprolol—An exploratory and descriptive study
Watson, S., Taavola, H., Attalla, M., Kaminsky, E., & Yue, Q. (2021) · Pharmacology Research & Perspectives · doi:10.1002/prp2.780 · Cited by 2
Application of biclustering algorithm in adverse drug reaction monitoring system of China
Zhu, T.-T., Zhang, Y., Ye, X., Hou, Y., Liu, J., Shi, W., Xu, J., Guo, X., & He, J. (2018) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4661 · Cited by 2
Updated Insights on Cardiac Risks of CD19-Directed Chimeric Antigen Receptor T-cell Therapy: A Pharmacovigilance Study
Zhai, Y., Hu, F., Zhu, B., Xu, J., Guo, X., Shi, W., Zhou, X., Zheng, Y., Xu, X., Ye, X., et al. (2023) · Immunotherapy · doi:10.2217/imt-2022-0145 · Cited by 2
Multiple Adverse Reactions Associated With the Use of Second-Generation Antipsychotics in People With Alzheimer’s Disease: A Pharmacovigilance Analysis Based on the FDA Adverse Event Reporting System
Zhou, J., Wei, Z., Huang, W., Liu, M., & Wu, X. (2024) · Annals of Pharmacotherapy · doi:10.1177/10600280241271093 · Cited by 3
Evaluation of post‐marketing safety using spontaneous reporting databases
Ahmed, I., Bégaud, B., & Tubert‐Bitter, P. (2014) · doi:10.1002/9781118763070.ch13 · Cited by 2
Quantitative methods to support drug benefit-risk assessment
Caster, O. (2014) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 2
Drug Safety in Children: Focus on hepatic concerns
Ferrajolo, C. (2014) · RePub (Erasmus University Rotterdam) · Cited by 2
Association evaluations of oral anticoagulants with dementia risk based on genomic and real-world data
Ma, J., Chen, H., Zou, C., & Yang, G. (2023) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2023.110929 · Cited by 1
Cardiovascular toxicities of hypoxia-inducible factor prolyl hydroxylase inhibitors: a disproportionality analysis based on JADER database
Wu, D., Xie, T., Jiao, S., Xie, P., Lin, H., & Yu, G. (2025) · Expert Opinion on Drug Safety · doi:10.1080/14740338.2025.2465873 · Cited by 1
The safety analysis of lurasidone based on the real-world data of FAERS database
Luo, Z., Zhang, M., Liang, W., Hu, M., Jiang, Y., Lei, X., Wang, Z., Zhu, J., Du, Y., Liu, Y., et al. (2025) · Expert Opinion on Drug Safety · doi:10.1080/14740338.2025.2498054 · Cited by 1
Pharmacovigilance analysis of myopathy associated with azoles and nonstatins interactions based on US FAERS database
Jing, S., Zhang, X., Zhang, Y., Sun, N., Du, X., Wang, X., Huang, X., Zhao, X., & Han, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-23424-x · Cited by 1
Co-occurrence and clustering characteristics of health risk behaviors among older adults in China
Sun, P., Gao, J., Xiao, T., Liang, X., Zhang, X., Zhang, X., Yan, X., & Ni, C. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1653090 · Cited by 1
Guillain–Barré syndrome associated with immune checkpoint inhibitors exposure: a pharmacovigilance study
Cabon, J., Ducloie, M., Chrétien, B., Arnaud, C., Alexandre, J., Dolladille, C., & Silva, A. D. (2025) · Journal of Neurology · doi:10.1007/s00415-025-13507-0 · Cited by 1
Mapping adverse events and monitoring priorities for trastuzumab deruxtecan use in metastatic breast cancer
Lin, Y., Su, Y., Huoshen, W., Lai, P., She, J., Zhao, R., Li, L., Zhang, Y., Wang, S., Lin, S., et al. (2026) · International Journal of Surgery · doi:10.1097/js9.0000000000004702 · Cited by 1
Global insights into [177Lu]Lu-DOTATATE safety: a comprehensive disproportionality analysis from the WHO pharmacovigilance database
Ladrière, T., Chrétien, B., Bignon, A.-L., Bouhier‐Leporrier, K., Dolladille, C., & Vigne, J. (2026) · Scientific Reports · doi:10.1038/s41598-026-36823-5 · Cited by 1
Drug-associated Takotsubo syndrome risk profiling: a global pharmacovigilance study
Tan, S. C. W., Loh, K. J. Q., Yin, Z.-W., Liu, Y.-S., Zhao, Y.-T., & Weng, C.-L. (2026) · Heart · doi:10.1136/heartjnl-2025-327329 · Cited by 1
Neutropenia and infectious events during off-label treatment with venetoclax in children with malignant disease: a pharmacovigilance analysis of FDA adverse event reporting system reports
Muratore, E., Giunchi, V., Fusaroli, M., Baccelli, F., Gottardi, F., Poluzzi, E., Masetti, R., & Raschi, E. (2026) · Annals of Hematology · doi:10.1007/s00277-026-06951-z · Cited by 1
Posterior Reversible Encephalopathy Syndrome with Angiogenesis Inhibitors for Solid Tumours: Clues from a Disproportionality Analysis of the FDA Adverse Event Reporting System and Pharmacodynamics
Donati, M., Cancellerini, C., Giunchi, V., Rossi, S., Massari, F., Gelsomino, F., Poluzzi, E., & Raschi, E. (2026) · Targeted Oncology · doi:10.1007/s11523-026-01222-3 · Cited by 1
The safety profile of lenalidomide, dexamethasone, daratumumab, and bortezomib combinations in multiple myeloma: a retrospective analysis of the FAERS database
Li, D., Xiao, Q., Yu, Z., Zheng, X., Hu, X., He, Y., Zhou, J., & Zheng, X. (2026) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-026-05472-w · Cited by 1
Absence of pharmacovigilance signals for bleeding events associated with warfarin-SSRI drug interactions: a comparative analysis of FAERS and CVARD databases
Zhang, P., Cheng, S., Diao, J., & Zhu, H. (2026) · Therapeutic Advances in Drug Safety · doi:10.1177/20420986261454180 · Cited by 1
Postmarketing Safety Signals of Microbiota-Based Live Biotherapeutic Products for Recurrent Clostridioides difficile Infection: A FDA Adverse Event Reporting System Pharmacovigilance Study
Gong, E. J., Bang, C. S., Lee, J. J., & Baik, G. H. (2026) · The American Journal of Gastroenterology · doi:10.14309/ajg.0000000000004133 · Cited by 1
Safety Signals of GLP ‐1 Receptor Agonists: A Multi‐Method Pharmacovigilance Analysis of FAERS (2018–2025) With Sensitivity‐Stratified Prioritisation, Notoriety‐Bias Assessment, and Cross‐Database Validation
Xu, S. (2026) · Diabetes Obesity and Metabolism · doi:10.1111/dom.71138 · Cited by 1
Persistent andexanet thromboembolic signals among reversal agents in FAERS
Chen, T., Luo, D., Xu, X., & Su, A. (2026) · Letters in Drug Design & Discovery · doi:10.1016/j.lddd.2026.100484 · Cited by 1
Combining Heterogeneous Databases to Detect Adverse Drug Reactions
Li, Y. (2015) · AMIA · doi:10.7916/d8z60nd1 · Cited by 2
Methods for drug safety signal detection using routinely collected observational electronic health care data: a systematic review
Motrinchuk, A. S., Loginovskaya, O. A., & Kolbatov, V. P. (2023) · Real-World Data & Evidence · doi:10.37489/2782-3784-myrwd-35 · Cited by 1
A Bayesian method to detect drug‐drug interaction using external information for spontaneous reporting system
Tada, K., Maruo, K., & Gosho, M. (2024) · Statistics in Medicine · doi:10.1002/sim.10137 · Cited by 1
Investigation of spontaneous abortion and stillbirth adverse events in epilepsy patients treated with levetiracetam: A pharmacovigilance study
Wang, Q., Sun, H., Huang, J., Chen, Y., Ni, J., Tang, Z., & Liu, J. (2024) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2024.110077 · Cited by 1
A real-world pharmacovigilance analysis of potential ototoxicity associated with sacubitril/valsartan based on FDA Adverse Event Reporting System (FAERS)
Wang, N., Wang, H., Chen, Y., & Wang, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-79272-8 · Cited by 1
Abortion adverse events associated with adalimumab, etanercept, ustekinumab, and dupilumab during pregnancy: A pharmacovigilance study based on FDA adverse event reporting system
Liu, Q., Wang, Y., Hu, P., & He, P. (2025) · Drug Discoveries & Therapeutics · doi:10.5582/ddt.2025.01043 · Cited by 1
Higher risk of gastrointestinal stenosis and obstruction in clozapine-treated individuals with schizophrenia: a pharmacovigilance study based on the FDA adverse event reporting system database
Li, D., Zhao, Z., Chen, Z., Qiao, Z., He, Y., Zhou, J., & Zheng, X. (2025) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-025-04481-5 · Cited by 1
Data Mining and Other Informatics Approaches to Pharmacoepidemiology
Bate, A., Trifirò, G., Avillach, P., & Evans, S. J. W. (2019) · Pharmacoepidemiology · doi:10.1002/9781119413431.ch27 · Cited by 1
Self-citation
Authors’ Response to Yoshihiro Noguchi’s Comment on: “A Disproportionality Analysis of Drug-Drug Interactions of Tizanidine and CYP1A2 Inhibitors from the FDA Adverse Event Reporting System (FAERS)”
Malone, D. C., Zapata, L. V., Gómez-Lumbreras, A., Horn, J. R., Tan, M. S., & Boyce, R. D. (2022) · Drug Safety · doi:10.1007/s40264-022-01239-3 · Cited by 1
Safety of Medication in Paediatrics
Star, K. (2013) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 1
Uncommon Data Sources for QSAR Modeling
Tropsha, A. (2018) · doi:10.1002/9783527806539.ch6l
ABSTRACTS: 12th ISoP Annual Meeting ‘New Landscapes for Pharmacovigilance’ Cancun, Mexico 30 October–2 November 2012
&NA; (2012) · Drug Safety · doi:10.2165/11640250-000000000-00000
Gastrointestinal infections are underestimated but fatal in chimeric antigen receptor T-cell therapy: a real-world study leveraging FDA adverse event reporting system from 2017 to 2021Preprint
Song, Z., Liu, Y., Xu, L., Liu, N., Yang, J., & Wang, Y. (2022) · Research Square · doi:10.21203/rs.3.rs-1786245/v1
Immune-related interstitial lung disease adverse event following immune checkpoint inhibitors and immune checkpoint inhibitors plus anti-VEGF antibody/chemotherapy: A real-world study from 2014 to 2022 based on FAERS databasesPreprint
Cui, C., Ren, X., Cui, W., & Wang, Y. (2022) · Research Square · doi:10.21203/rs.3.rs-2023720/v1
Reply to: Comments on “Adverse reactions associated with immune checkpoint inhibitors and bevacizumab: A pharmacovigilance analysis”
Gu, T., Lin, A., Wang, H., Zhang, J., & Luo, P. (2023) · International Journal of Cancer · doi:10.1002/ijc.34497
Comment on: Drugs That Interact With Colchicine via Inhibition of Cytochrome P450 3A4 and P-Glycoprotein: A Signal Detection Analysis Using a Database of Spontaneously Reported Adverse Events (FAERS)
Noguchi, Y., Yan, M., & Yoshimura, T. (2023) · Annals of Pharmacotherapy · doi:10.1177/10600280231168858
A Pharmacoepidemiological Approach For Evaluating The Risk Of Myocarditis And Pericarditis Following Mrna COVID-19 Vaccination
Alami, A. (2023) · Carleton University's Institutional Repository (MacOdrum Library, Carleton University) · doi:10.22215/etd/2023-15810
Reporting of tumor lysis syndrome with targeted therapy for hepatic cancer in the FDA adverse events reporting system
Liu, L., Zhang, C., Liu, H., Luo, H., Cao, R., Xiao, M., Zou, P., Xie, Z., Gong, H., Ma, R., et al. (2024) · Expert Opinion on Drug Safety · doi:10.1080/14740338.2024.2312147
A real-world disproportionality analysis of Amiodarone: data mining of the public version of FDA adverse event reporting systemPreprint
Xu, Y., Zhao, B., & Li-qun, H. (2024) · Research Square · doi:10.21203/rs.3.rs-4378458/v1
pvda: Disproportionality Functions for Pharmacovigilance
Gauffin, O. (2024) · doi:10.32614/cran.package.pvda
Improving the observed-to-expected ratio method to identify true patterns of multimorbidityPreprint
Baneshi, M. R., Mishra, G. D., & Dobson, A. J. (2024) · Research Square · doi:10.21203/rs.3.rs-4954110/v1
Mifepristone-related foetal cardiac adverse events: findings from the postmarketing safety reports
Liu, R., Wang, J., Wang, Q., Zhang, Y., & Yuan, F. (2025) · Journal of Obstetrics and Gynaecology · doi:10.1080/01443615.2025.2528093
Early Detection of Adverse Drug Reactions in Postmarket Monitoring
Duan, L., Zhou, W., Hu, Y. Q., Xu, L. D., & Liu, M. (2025) · INFORMS journal on computing · doi:10.1287/ijoc.2024.0585

Authors: Norén, G. N., Hopstadius, J., & Bate, A.
Affiliations: Uppsala Monitoring Centre (SE)
Corresponding author: Norén, G Niklas
ORCID: Norén, G Niklas
Bate, Andrew
Venue: Statistical Methods in Medical Research
Publisher: SAGE Publications
Published: 2011-06-24
Volume: 22
Issue: 1
Pages: 57-69
ISSN: 0962-2802 (ISSN-L)
1477-0334
DOI: 10.1177/0962280211403604
PMC: PMC6331976
OpenAlex: W2005366883
Cited by: 336
References: 26
Citations by year: 2026: 45 · 2025: 45 · 2024: 48 · 2023: 46 · 2022: 53 · 2021: 21 · 2020: 17 · 2019: 9 · 2018: 11 · 2017: 5 · 2016: 5 · 2015: 8 · 2014: 10 · 2013: 7 · 2012: 6
FWCI: 6.43
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Statistical Methods in Medical Research (publishedVersion, cc-by-nc)
Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
PubMed Central (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords: observed-to-expected ratio, pattern discovery, observational data, spurious association, rare events, higher-order interactions, temporal association, associative strength, clinical practice
MeSH terms: Models, Statistical

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks