Can We Rely on Results From IQVIA Medical Research Data UK Converted to the Observational Medical Outcome Partnership Common Data Model?

Abstract

Exploring and combining results from more than one real‐world data (RWD) source might be necessary in order to explore variability and demonstrate generalizability of the results or for regulatory requirements. However, the heterogeneous nature of RWD poses challenges when working with more than one source, some of which can be solved by analyzing databases converted into a common data model (CDM). The main objective of the study was to evaluate the implementation of the Observational Medical Outcome Partnership (OMOP) CDM on IQVIA Medical Research Data (IMRD)‐UK data. A drug utilization study describing the prescribing of codeine for pain in children was used as a case study to be replicated in IMRD‐UK and its corresponding OMOP CDM transformation. Differences between IMRD‐UK source and OMOP CDM were identified and investigated. In IMRD‐UK updated to May 2017, results were similar between source and transformed data with few discrepancies. These were the result of different conventions applied during the transformation regarding the date of birth for children younger than 15 years and the start of the observation period, and of a misclassification of two drug treatments. After the initial analysis and feedback provided, a rerun of the analysis in IMRD‐UK updated to September 2018 showed almost identical results for all the measures analyzed. For this study, the conversion to OMOP CDM was adequate. Although some decisions and mapping could be improved, these impacted on the absolute results but not on the study inferences. This validation study supports six recommendations for good practice in transforming to CDMs.

Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment

“…There is also potential for information loss during the process of mapping from the source data to the common data model and standardised vocabularies [62]. Several validation studies have been published describing both successes and challenges in mapping data to OMOP [18,[53][54][55][62][63][64][65]. For common data elements such as drugs and conditions, mapping can usually be performed with high fidelity.…”

“…The European Medicines Agency (EMA) will use the OMOP common data model to conduct multicentre cohort studies on the use of medicines in patients with coronavirus 2019 (COVID-19) [16]. The EMA are also looking to establish a data network for the proactive monitoring of benefit-risk profiles of new medicines over their life cycles, which could use a common data model approach [6,17,18]. To date, relatively little attention has been given to the usefulness of these models and data networks for supporting health technology assessment (HTA).…”

Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned

“…Several actions can support and improve the transformation process. One of them is communication and collaboration with different experts and consortiums [36] It is mandatory to consult with the medical personnel to understand the clinical practice and map the codes correctly. Still, in our case, despite our efforts and the inclusion of medical experts, more than half of the local codes remained unmapped (61%).…”

Extract, Transform, Load Framework for the Conversion of Health Databases to OMOP

“…Most publications reporting on the mapping of specific health datasets to OMOP, simply document the data they map, challenges encountered, and data validation of the mapped data [6][7][8][9][10][11]23]. Some studies report replications of studies comparing results with the source data and the data mapped to OMOP (to identify biases or discrepancies introduced as part of the translational process) [24]. However, these are of limited value to health institutions or research groups attempting to map third datasets to OMOP.…”