Visualizations throughout pharmacoepidemiology study planning, implementation, and reporting

Gatto, Nicolle M.; Wang, Shirley; Murk, William; Mattox, Pattra; Brookhart, M. Alan; Bate, Andrew; Schneeweiß, Sebastian; Rassen, Jeremy A.

doi:10.1002/pds.5529

Cited by 12 publications

(7 citation statements)

References 60 publications

(89 reference statements)

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“…For this study we used data visualization tools available in the OHDSI ATLAS interface. Future research could explore the adoption of other tools [ 47 , 48 ] or develop novel ones tailored to the signal management process. Lastly, we were only able to consider analyses in the routine health data for a small set of signals (8 in total) and we did not systematically assess all possible analyses that could have informed decision making for each of these.…”

Section: Discussionmentioning

confidence: 99%

Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study

Gauffin,

Brand,

Vidlin

et al. 2023

Drug Saf

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Introduction Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals. Objective The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process. Methods Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals. Results Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15–60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine health data. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development. Conclusions Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost–benefit of integrating these analyses at this stage of signal management requires further research.

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Section: Discussionmentioning

confidence: 99%

Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study

Gauffin,

Brand,

Vidlin

et al. 2023

Drug Saf

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“…Furthermore, the structured methodology for identifying fitfor-purpose data, as recently proposed by Gatto and colleagues, 15,16,17 underscores the necessity of having comprehensive information about potential data sources. This requirement is to ensure they meet the minimum criteria for validly capturing design elements.…”

Section: Plan Methods To Address Data Diversitymentioning

confidence: 99%

Describing diversity of real world data sources in pharmacoepidemiologic studies: The DIVERSE scoping review

Gini,

Pajouheshnia,

Gardarsdottir

et al. 2024

Pharmacoepidemiology and Drug

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PurposeReal‐world evidence (RWE) is increasingly used for medical regulatory decisions, yet concerns persist regarding its reproducibility and hence validity. This study addresses reproducibility challenges associated with diversity across real‐world data sources (RWDS) repurposed for secondary use in pharmacoepidemiologic studies. Our aims were to identify, describe and characterize practices, recommendations and tools for collecting and reporting diversity across RWDSs, and explore how leveraging diversity could improve the quality of evidence.MethodsIn a preliminary phase, keywords for a literature search and selection tool were designed using a set of documents considered to be key by the coauthors. Next, a systematic search was conducted up to December 2021. The resulting documents were screened based on titles and abstracts, then based on full texts using the selection tool. Selected documents were reviewed to extract information on topics related to collecting and reporting RWDS diversity. A content analysis of the topics identified explicit and latent themes.ResultsAcross the 91 selected documents, 12 topics were identified: 9 dimensions used to describe RWDS (organization accessing the data source, data originator, prompt, inclusion of population, content, data dictionary, time span, healthcare system and culture, and data quality), tools to summarize such dimensions, challenges, and opportunities arising from diversity. Thirty‐six themes were identified within the dimensions. Opportunities arising from data diversity included multiple imputation and standardization.ConclusionsThe dimensions identified across a large number of publications lay the foundation for formal guidance on reporting diversity of data sources to facilitate interpretation and enhance replicability and validity of RWE.

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“…Sankey plots that document patient flow between treatment options is gaining popularity in the field of pharmacoepidemiology to visualize treatment transition and switching patterns ( Gatto et al, 2022 ). Fig.…”

Section: Identifying Osteoporosis Medication Exposures In Rwdmentioning

confidence: 99%

Methodological guidance for the use of real-world data to measure exposure and utilization patterns of osteoporosis medications

Hayes,

Cadarette,

Burden

2024

Bone Reports

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Visualizations throughout pharmacoepidemiology study planning, implementation, and reporting

Cited by 12 publications

References 60 publications

Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study

Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study

Describing diversity of real world data sources in pharmacoepidemiologic studies: The DIVERSE scoping review

Methodological guidance for the use of real-world data to measure exposure and utilization patterns of osteoporosis medications

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