The United States Prescribing Information (USPI), also known as the Food and Drug Administration (FDA) drug label, serves as a primary source of information for healthcare professionals to access reliable information on drug benefits and risks. Within the drug label, adverse events are listed across varying sections (e.g., Contraindications, Warnings and Precautions, Adverse Reactions). However, at present it is unclear what evidentiary thresholds FDA applies before including specific adverse event types in various sections of the drug label. To examine this question, we conducted an analysis of United States (US) regulations, guidance documents, standard operating procedures and policies published by FDA pertaining to the assessment of adverse events and drug labeling. This study examines how causality assessments shape decisions regarding the inclusion and placement of adverse events in the US drug label. We analyzed US regulations and policy documents published by FDA up to June 2024. Sources included the electronic Code of Federal Regulations, FDA policy documents (e.g., guidance documents, standard operating procedures, and policies) identified through searches of the FDA website and Westlaw legal database. Documents were analyzed using ATLAS.ti to extract and compare causality-related language across drug labeling sections. Our analysis identified heterogeneous and sometimes contradictory causality assessment evidentiary thresholds across US drug label sections. We found no predefined or standardized causality thresholds specified in FDA regulations, allowing interpretive flexibility that may contribute to variability and opacity in labeling decisions. For example, the Adverse Reactions section requires “some basis to believe” a causal relationship, while the Warnings and Precautions section states that “reasonable evidence” of causality is needed for serious events. We recommend greater standardization of causality-related terminology and public documentation of evidentiary rationales supporting labeling decisions. Improved clarity regarding evidence certainty would better support prescribers and patients in understanding drug-related harms and strengthen public trust in regulatory drug safety assessments.
Disproportionality signal detection methods can help identify potential adverse outcomes of medication exposure. Such methods scan all medication–outcome pairs within a dataset to identify possible associations requiring further investigation. Many signal detection methods use separate statistical tests for each pair, however, information sharing between groups of ‘similar’ medications or outcomes may produce more sensitive and specific detection of harmful effects. Data-defined groups identified through clustering may provide a better reflection of dependencies within the data. We present a new signal detection method that combines the information component, biclustering and Bayesian hierarchical modelling (IC–Bic–BHM) to identify groups within the data and use these groups within the analysis. We use simulations to demonstrate the importance of using groupings that accurately reflect dependency within the data; and compare sensitivity, specificity, positive predictive value and negative predictive value of the IC–Bic–BHM with other methods. Results of application to 536 medications and 61 types of congenital anomaly in babies/foetuses from 87,513 EUROmediCAT pregnancies were also compared. Sensitivity and specificity of Bayesian hierarchical models are dependent upon how accurately groupings reflect dependency within the data. The IC-Bic-BHM is the first method to use data-defined dependencies between medications and outcomes in signal detection. Simulations suggest it performs well in datasets with large numbers of medication–outcome pairs but requires adequate power to detect groups. The IC-Bic-BHM identified signals involving several known teratogenic medications within EUROmediCAT data. This study presents a novel approach to signal detection but requires further investigation before adoption.
Retrieval of adverse event reports based on coded drug–event co-occurrence enables large-scale pharmacovigilance analyses but yields candidate reports rather than clinically adjudicated cases. Without explicit case identification, heterogeneous reports may be combined within the same case series and misinterpreted in downstream analyses. The aim of the study was to develop a structured framework for distinguishing clinically meaningful cases from candidate reports retrieved through coded drug–event co-occurrence, and to illustrate its application across information-rich and routine-review conditions. We conducted a sequential framework-development study using two case studies. Drug-induced impulsivity was used to iteratively develop and refine the framework under information-rich conditions. Drug-induced suicidality was used to examine its application under routine-review conditions with variable information availability and explicit classification of nonassessability. In case 1, nonrelevant reports were more frequent for drugs with uncertain or no established evidence ( 20–40
In the absence of a gold standard for assessing the completeness of spontaneous adverse drug reaction (ADR) reports, most studies have relied on assessing the proportion of reports containing information on predefined categories. The same principle applies to the Vigi4Eudra-score, which can be used to analyse the completeness of ADR reports from EudraVigilance. Moreover, ADR reports with a high level of information provided may be more useful in pharmacovigilance practice. The first aim of our study was to further develop the already existing Vigi4Eudra-score by modifying its penalties and by adding additional relevant categories (= EudraScore). The second aim was to assess whether the EudraScore performed better than the Vigi4Eudra-score to identify reports with at least a possible causal relationship as “well documented”. An expert panel was established comprising assessors from the pharmacovigilance and the research department of the German Federal Institute for Drugs and Medical Devices, with many years of experience in the assessment of ADR reports. Based on expert consensus, the Vigi4Eudra-score was modified by adapting existing categories and incorporating additional causality-relevant criteria, resulting in the EudraScore. A use case was conducted to compare the values calculated using the EudraScore with those obtained from the Vigi4Eudra-score. The performance of both scores in identifying reports with an “at least possible” causal relationship as “well documented” was evaluated by calculating sensitivity and specificity. Compared to the Vigi4Eudra-score, some penalties were adjusted for the EudraScore, with reduced penalties for missing country of origin and drug dose (from 10 to 5
