{"title":"Observed Versus Expected Analysis-How Does It Fit in the Pharmacovigilance Toolkit?","authors":"Lionel Van Holle","doi":"10.1007/s40264-025-01584-z","DOIUrl":null,"url":null,"abstract":"<p><p>Observed versus expected (O/E) analyses have been used in an unprecedented scale for the safety monitoring of the COVID-19 mass vaccination. The extent of their usage changed its nature, which consisted of a mixture of medical expertise and epidemiology, into something more algorithmic and automated. By doing so, the observed versus expected analysis became closer to disproportionality analysis (DPA), which is also a type of observed versus expected analysis that differs in the way the expected is calculated. A qualitative assessment of the strengths and limitations of both methods concludes that the algorithmic O/E is more likely to underestimate under-reporting, is more likely to be sensitive to asymmetrical differences in the definition of the condition of interest, and is more dependent on a greater variety of data sources or medical knowledge that might not be accurate for emerging safety issues (exposure, background incidence rate, and risk window). Provided some adjustment (stratification and/or subgrouping) of the routine disproportionality into a targeted disproportionality occurs, which would account for the epidemiological specifics of the vaccine and event-of-interest, the targeted DPA has the potential to be promoted from a signal detection method into a signal evaluation method that could advantageously replace the algorithmic O/E analysis. Research on the setup of a sensitivity analysis framework integrating several standardized choices of disproportionality settings, along with measures (qualitative or quantitative) of the biases for each choice, could be more beneficial for the pharmacovigilance field than studies designed to estimate the background incidence rates of adverse events of special interest for the sole purpose of being used in O/E analyses.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drug Safety","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s40264-025-01584-z","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
Observed versus expected (O/E) analyses have been used in an unprecedented scale for the safety monitoring of the COVID-19 mass vaccination. The extent of their usage changed its nature, which consisted of a mixture of medical expertise and epidemiology, into something more algorithmic and automated. By doing so, the observed versus expected analysis became closer to disproportionality analysis (DPA), which is also a type of observed versus expected analysis that differs in the way the expected is calculated. A qualitative assessment of the strengths and limitations of both methods concludes that the algorithmic O/E is more likely to underestimate under-reporting, is more likely to be sensitive to asymmetrical differences in the definition of the condition of interest, and is more dependent on a greater variety of data sources or medical knowledge that might not be accurate for emerging safety issues (exposure, background incidence rate, and risk window). Provided some adjustment (stratification and/or subgrouping) of the routine disproportionality into a targeted disproportionality occurs, which would account for the epidemiological specifics of the vaccine and event-of-interest, the targeted DPA has the potential to be promoted from a signal detection method into a signal evaluation method that could advantageously replace the algorithmic O/E analysis. Research on the setup of a sensitivity analysis framework integrating several standardized choices of disproportionality settings, along with measures (qualitative or quantitative) of the biases for each choice, could be more beneficial for the pharmacovigilance field than studies designed to estimate the background incidence rates of adverse events of special interest for the sole purpose of being used in O/E analyses.
期刊介绍:
Drug Safety is the official journal of the International Society of Pharmacovigilance. The journal includes:
Overviews of contentious or emerging issues.
Comprehensive narrative reviews that provide an authoritative source of information on epidemiology, clinical features, prevention and management of adverse effects of individual drugs and drug classes.
In-depth benefit-risk assessment of adverse effect and efficacy data for a drug in a defined therapeutic area.
Systematic reviews (with or without meta-analyses) that collate empirical evidence to answer a specific research question, using explicit, systematic methods as outlined by the PRISMA statement.
Original research articles reporting the results of well-designed studies in disciplines such as pharmacoepidemiology, pharmacovigilance, pharmacology and toxicology, and pharmacogenomics.
Editorials and commentaries on topical issues.
Additional digital features (including animated abstracts, video abstracts, slide decks, audio slides, instructional videos, infographics, podcasts and animations) can be published with articles; these are designed to increase the visibility, readership and educational value of the journal’s content. In addition, articles published in Drug Safety Drugs may be accompanied by plain language summaries to assist readers who have some knowledge of, but not in-depth expertise in, the area to understand important medical advances.