Observational studyClinical pharmacology and therapeutics2020
Can We Rely on Results From IQVIA Medical Research Data UK Converted to the Observational Medical Outcome Partnership Common Data Model?: A Validation Study Based on Prescribing Codeine in Children.
Observational study in Clinical pharmacology and therapeutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Common data models and data standards for tabular health data: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Evaluating Frailty Index Integrity: Insights from an International Network Study.medRxiv : the preprint server for health sciences · 2026Article
- Gender and contemporary risk of adverse events in atrial fibrillation.European heart journal · 2024Article
- Seasonal variation of serum potassium and related prescription pattern: an ecological time series.Journal of clinical pathology · 2024Article
- Thromboembolic events and vascular dementia in patients with atrial fibrillation and low apparent stroke risk.Nature medicine · 2024Article
- OHDSI Standardized Vocabularies-a large-scale centralized reference ontology for international data harmonization.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Developing RxNorm Extension: A Step Toward Global Drug Data Harmonization in Observational Drug Research.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned.JAMIA open · 2023Article
- Challenges to Using Big Data in Cancer.Cancer research · 2023Review
- Deep-learning-based automated terminology mapping in OMOP-CDM.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment.PharmacoEconomics · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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