Exploring the impact of design criteria for reference sets on performance evaluation of signal detection algorithms: The case of drug–drug interactions

Purpose To evaluate the impact of multiple design criteria for reference sets that are used to quantitatively assess the performance of pharmacovigilance signal detection algorithms (SDAs) for drug–drug interactions (DDIs). Methods Starting from a large and diversified reference set for two‐way DDIs...

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Bibliographic Details
Published in:Pharmacoepidemiology and drug safety Vol. 32; no. 8; pp. 832 - 844
Main Authors: Kontsioti, Elpida, Maskell, Simon, Pirmohamed, Munir
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Inc 01.08.2023
Wiley Subscription Services, Inc
Subjects:
Adverse Drug Reaction Reporting Systems
Adverse events
Algorithms
Business metrics
Databases, Factual
Drug development
Drug interaction
Drug Interactions
Drug-Related Side Effects and Adverse Reactions - diagnosis
Drug-Related Side Effects and Adverse Reactions - epidemiology
drug–drug interactions
Humans
Original
performance metrics
Pharmacovigilance
postmarketing surveillance
signal detection
spontaneous reports data
United States
United States Food and Drug Administration
United States
postmarketing surveillance
drug-drug interactions
signal detection
performance metrics
spontaneous reports data
adverse events
pharmacovigilance
ISSN:1053-8569, 1099-1557, 1099-1557
Online Access:Get full text
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Summary:Purpose To evaluate the impact of multiple design criteria for reference sets that are used to quantitatively assess the performance of pharmacovigilance signal detection algorithms (SDAs) for drug–drug interactions (DDIs). Methods Starting from a large and diversified reference set for two‐way DDIs, we generated custom‐made reference sets of various sizes considering multiple design criteria (e.g., adverse event background prevalence). We assessed differences observed in the performance metrics of three SDAs when applied to FDA Adverse Event Reporting System (FAERS) data. Results For some design criteria, the impact on the performance metrics was neglectable for the different SDAs (e.g., theoretical evidence associated with positive controls), while others (e.g., restriction to designated medical events, event background prevalence) seemed to have opposing and effects of different sizes on the Area Under the Curve (AUC) and positive predictive value (PPV) estimates. Conclusions The relative composition of reference sets can significantly impact the evaluation metrics, potentially altering the conclusions regarding which methodologies are perceived to perform best. We therefore need to carefully consider the selection of controls to avoid misinterpretation of signals triggered by confounding factors rather than true associations as well as adding biases to our evaluation by “favoring” some algorithms while penalizing others.
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ISSN:1053-8569
1099-1557
1099-1557
DOI:10.1002/pds.5609