IncidencePrevalence : An R package to calculate population-level incidence rates and prevalence using the OMOP common data model
Raventós, Berta (Universitat Autònoma de Barcelona)
Català, Martí (University of Oxford)
Du, Mike (University of Oxford)
Guo, Yuchen (University of Oxford)
Black, Adam (Odysseus Data Services)
Inberg, Ger (Department of Medical Informatics. Erasmus University Medical Center)
Li, Xintong (University of Oxford)
López-Güell, Kim (University of Oxford)
Newby, Danielle (University of Oxford)
de Ridder, María (Erasmus University Medical Center)
Barboza, César (Erasmus University Medical Center)
Duarte-Salles, Talita 1985- (Erasmus University Medical Center)
Verhamme, Katia (Erasmus University Medical Center)
Rijnbeek, Peter (Erasmus University Medical Center)
Prieto Alhambra, Danielle (Erasmus University Medical Center)
Burn, Edward (University of Oxford)

Data:	2024
Resum:	Purpose: Real-world data (RWD) offers a valuable resource for generating population-level disease epidemiology metrics. We aimed to develop a well-tested and user-friendly R package to compute incidence rates and prevalence in data mapped to the observational medical outcomes partnership (OMOP) common data model (CDM). Materials and Methods: We created IncidencePrevalence, an R package to support the analysis of population-level incidence rates and point- and period-prevalence in OMOP-formatted data. On top of unit testing, we assessed the face validity of the package. To do so, we calculated incidence rates of COVID-19 using RWD from Spain (SIDIAP) and the United Kingdom (CPRD Aurum), and replicated two previously published studies using data from the Netherlands (IPCI) and the United Kingdom (CPRD Gold). We compared the obtained results to those previously published, and measured execution times by running a benchmark analysis across databases. Results: IncidencePrevalence achieved high agreement to previously published data in CPRD Gold and IPCI, and showed good performance across databases. For COVID-19, incidence calculated by the package was similar to public data after the first-wave of the pandemic. Conclusion: For data mapped to the OMOP CDM, the IncidencePrevalence R package can support descriptive epidemiological research. It enables reliable estimation of incidence and prevalence from large real-world data sets. It represents a simple, but extendable, analytical framework to generate estimates in a reproducible and timely manner.
Nota:	Development of the IncidencePrevalence R package was funded by the European Medicines Agency as part of the Data Analysis and Real World Interrogation Network (DARWIN EU®). This manuscript represents the views of the DARWIN EU® Coordination Centre only and cannot be interpreted as reflecting the views of the European Medicines Agency or the European Medicines Regulatory Network.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Common data model ; Incidence ; OMOP ; Prevalence ; R package
Publicat a:	Pharmacoepidemiology and Drug Safety, Vol. 33 Núm. 1 (january 2024) , p. e5717, ISSN 1099-1557

DOI: 10.1002/pds.5717

11 p, 1.9 MB

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