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Generalised weibull model-based approaches to detect non-constant hazard to signal adverse drug reactions in longitudinal data

Pharmacovigilance is the process of monitoring the emergence of harm from a medicine once it has been licensed and is in use. The aim is to identify new adverse drug reactions (ADRs) or changes in frequency of known ADRs. The last decade has seen increased interest for the use of electronic health r...

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Autores principales: Sauzet, Odile, Cornelius, Victoria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445551/
https://www.ncbi.nlm.nih.gov/pubmed/36081935
http://dx.doi.org/10.3389/fphar.2022.889088
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author Sauzet, Odile
Cornelius, Victoria
author_facet Sauzet, Odile
Cornelius, Victoria
author_sort Sauzet, Odile
collection PubMed
description Pharmacovigilance is the process of monitoring the emergence of harm from a medicine once it has been licensed and is in use. The aim is to identify new adverse drug reactions (ADRs) or changes in frequency of known ADRs. The last decade has seen increased interest for the use of electronic health records (EHRs) in pharmacovigilance. The causal mechanism of an ADR will often result in the occurrence being time dependent. We propose identifying signals for ADRs based on detecting a variation in hazard of an event using a time-to-event approach. Cornelius et al. proposed a method based on the Weibull Shape Parameter (WSP) and demonstrated this to have optimal performance for ADRs occurring shortly after taking treatment or delayed ADRs, and introduced censoring at varying time points to increase performance for intermediate ADRs. We now propose two new approaches which combined perform equally well across all time periods. The performance of this new approach is illustrated through an EHR Bisphosphonates dataset and a simulation study. One new approach is based on the power generalised Weibull distribution (pWSP) introduced by Bagdonavicius and Nikulin alongside an extended version of the WSP test, which includes one censored dataset resulting in improved detection across time period (dWSP). In the Bisphosphonates example, the pWSP and dWSP tests correctly signalled two known ADRs, and signal one adverse event for which no evidence of association with the drug exist. A combined test involving both pWSP and dWSP is reliable independently of the time of occurrence of ADRs.
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spelling pubmed-94455512022-09-07 Generalised weibull model-based approaches to detect non-constant hazard to signal adverse drug reactions in longitudinal data Sauzet, Odile Cornelius, Victoria Front Pharmacol Pharmacology Pharmacovigilance is the process of monitoring the emergence of harm from a medicine once it has been licensed and is in use. The aim is to identify new adverse drug reactions (ADRs) or changes in frequency of known ADRs. The last decade has seen increased interest for the use of electronic health records (EHRs) in pharmacovigilance. The causal mechanism of an ADR will often result in the occurrence being time dependent. We propose identifying signals for ADRs based on detecting a variation in hazard of an event using a time-to-event approach. Cornelius et al. proposed a method based on the Weibull Shape Parameter (WSP) and demonstrated this to have optimal performance for ADRs occurring shortly after taking treatment or delayed ADRs, and introduced censoring at varying time points to increase performance for intermediate ADRs. We now propose two new approaches which combined perform equally well across all time periods. The performance of this new approach is illustrated through an EHR Bisphosphonates dataset and a simulation study. One new approach is based on the power generalised Weibull distribution (pWSP) introduced by Bagdonavicius and Nikulin alongside an extended version of the WSP test, which includes one censored dataset resulting in improved detection across time period (dWSP). In the Bisphosphonates example, the pWSP and dWSP tests correctly signalled two known ADRs, and signal one adverse event for which no evidence of association with the drug exist. A combined test involving both pWSP and dWSP is reliable independently of the time of occurrence of ADRs. Frontiers Media S.A. 2022-08-23 /pmc/articles/PMC9445551/ /pubmed/36081935 http://dx.doi.org/10.3389/fphar.2022.889088 Text en Copyright © 2022 Sauzet and Cornelius. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Sauzet, Odile
Cornelius, Victoria
Generalised weibull model-based approaches to detect non-constant hazard to signal adverse drug reactions in longitudinal data
title Generalised weibull model-based approaches to detect non-constant hazard to signal adverse drug reactions in longitudinal data
title_full Generalised weibull model-based approaches to detect non-constant hazard to signal adverse drug reactions in longitudinal data
title_fullStr Generalised weibull model-based approaches to detect non-constant hazard to signal adverse drug reactions in longitudinal data
title_full_unstemmed Generalised weibull model-based approaches to detect non-constant hazard to signal adverse drug reactions in longitudinal data
title_short Generalised weibull model-based approaches to detect non-constant hazard to signal adverse drug reactions in longitudinal data
title_sort generalised weibull model-based approaches to detect non-constant hazard to signal adverse drug reactions in longitudinal data
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445551/
https://www.ncbi.nlm.nih.gov/pubmed/36081935
http://dx.doi.org/10.3389/fphar.2022.889088
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