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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Veröffentlicht in:	Pharmacoepidemiology and drug safety Jg. 32; H. 8; S. 832 - 844
Hauptverfasser:	Kontsioti, Elpida, Maskell, Simon, Pirmohamed, Munir
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Chichester, UK John Wiley & Sons, Inc 01.08.2023 Wiley Subscription Services, Inc
Schlagworte:	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
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Abstract	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.
AbstractList	PurposeTo 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).MethodsStarting 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.ResultsFor 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.ConclusionsThe 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. 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). 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. 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. 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. 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).PURPOSETo 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).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.METHODSStarting 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.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.RESULTSFor 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.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.CONCLUSIONSThe 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. 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.
Author	Kontsioti, Elpida Pirmohamed, Munir Maskell, Simon
AuthorAffiliation	1 Department of Electrical Engineering and Electronics University of Liverpool Liverpool UK 2 The Wolfson Center for Personalized Medicine, Center for Drug Safety Science, Department of Pharmacology and Therapeutics Institute of Systems, Molecular and Integrative Biology, University of Liverpool Liverpool UK
AuthorAffiliation_xml	– name: 2 The Wolfson Center for Personalized Medicine, Center for Drug Safety Science, Department of Pharmacology and Therapeutics Institute of Systems, Molecular and Integrative Biology, University of Liverpool Liverpool UK – name: 1 Department of Electrical Engineering and Electronics University of Liverpool Liverpool UK
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/36916014$$D View this record in MEDLINE/PubMed
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Copyright	2023 The Authors. published by John Wiley & Sons Ltd. 2023 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd. 2023. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Keywords	postmarketing surveillance drug-drug interactions signal detection performance metrics spontaneous reports data adverse events pharmacovigilance
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Snippet	Purpose To evaluate the impact of multiple design criteria for reference sets that are used to quantitatively assess the performance of pharmacovigilance... To evaluate the impact of multiple design criteria for reference sets that are used to quantitatively assess the performance of pharmacovigilance signal... PurposeTo evaluate the impact of multiple design criteria for reference sets that are used to quantitatively assess the performance of pharmacovigilance signal...
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SubjectTerms	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
Title	Exploring the impact of design criteria for reference sets on performance evaluation of signal detection algorithms: The case of drug–drug interactions
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