Europe’s lead regulatory authority is very active in working with real work data (RWD) in its pursuit of its statutory goals of:
- Facilitate development and access to medicines
- Evaluate applications for marketing authorisation
- Monitor the safety of medicines across the life cycle
- Provide reliable information to patients and the public
EMA is working towards and collaborating with multiple stakeholders on a Learning Healthcare System, and in particular on disease epidemiology, pharmacovigilance and product-related surveillance.
While using RWD to generate Real World Evidence (RWE), EMA has conducted 88 in-house studies from 2013 to 2019 using various databases available in Europe. A further 19 external studies were conducted to support EMA committees from 2010 to 2019.
Recently, two publications were published via EMA in this domain which are very relevant to EHDEN, OHDSI and the field of RWD/RWE.
In a paper published in Clinical Pharmacology & Therapeutics on 19th January, entitled, ‘Can we rely on results from IQVIA Medical Research Data UK converted to the Observational Medical Outcome Partnership Common Data Model?’, Gianmario Candore and colleagues evaluated the fidelity of common data model (CDM) mapped data versus original source data in drug utilisation study describing the prescribing of codeine for pain in children.
The authors state, ‘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 re‐run of the analysis in IMRD‐UK updated to September 2018 showed almost identical results for all the measures analysed.’
‘For this study, the conversion to OMOP CDM was adequate. While 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’.
This is a valuable paper addressing one of the more common questions asked about the use of the OMOP common data model to harmonise source data prior to conducting studies.
Of a wider, more strategic import, the EMA-HMA Big Data Taskforce published its report and recommendations on 20th January on, ‘Evolving Data-Driven Regulation’ and proposes ten recommendations, in particular a network proposition, DARWIN (‘Data Analysis and Real World Interrogation Network’):
- Deliver a sustainable platform to access and analyse healthcare data from across the EU (DARWIN)
- Establish an EU framework for data quality and representativeness
- Enable data discoverability
- Develop EU network skills in Big Data
- Strengthen EU network processes for Big Data submissions
- Build EU network capability to analyse Big Data
- Modernise the delivery of expert advice
- Ensure data are managed and analysed within a secure and ethical governance framework
- Collaborate with international initiatives on Big Data
- Create an EU Big Data ‘stakeholder implementation forum’
There is more detail in the report, but EHDEN would be entirely supportive of its aims and objectives as typified in the final paragraph of the conclusions, ‘Clearly, we must not desert well proven, robust regulatory models designed to eliminate bias indecision-making, but equally we need to strengthen and adapt our currently regulatory model so we are able to confidently extract value out of the data to address the assessment challenges ahead. It is clear that the data landscape is evolving, and that the regulatory system needs to evolve also. In this way we can realise opportunities for public health and innovation through better evidence for decisions on the development, authorisation and on-market safety and effectiveness monitoring of medicines. As healthcare data and technology evolve then so must medicines regulation.’
EHDEN has very much been pleased to collaborate with EMA colleagues to date, and indeed EMA-HMA Big Data Taskforce colleagues are also on the EHDEN Scientific Advisory Board, and we look forward to exploring convergent use cases of interest and collaborating on the development of federated data and analytical federated networks together.