Real-World Data for Regulatory Decision Making in Europe

Real-World Data for Regulatory Decision Making in Europe

An Ranquin Posted 28 October 2019

There is a growing interest in the use of real world data to support regulatory decision making. Real world data and real world evidence has the potential to provide additional insights into treatment effects in more diverse clinical settings as opposed to traditional randomised clinical trials (RCTs). For disease areas where RCTs may be unfeasible or unethical (e.g. rare diseases, specific unmet medical needs), real world evidence may even be detrimental to obtaining regulatory approvals.

Real world data may come from different sources such as electronic health records, insurance claims, prescription data, patient registries, observational trials, data from wearables, m-health apps, and environmental data.

Although Europe is rich in healthcare data, its use in supporting regulatory decision making is limited due to the heterogeneous nature of the data. In order to fully harvest the potential of real world data in Europe, operational, technical and methodological challenges need to be addressed (Cave A, Kurz X, Arlett, P. Clinical pharmacology and therapeutics; 2019; 16(1)).

These challenges include:

  • Data access
  • Data availability
  • Cost
  • Different legal and ethical requirement for sharing data
  • Data protection
  • Sustained data collection and analysis
  • Differences in datasets (terminology, format, quality and content)
  • Missing data leading to confounders and bias

Addressing these challenges will require a multistakeholder approach to streamline data sharing policies, standardize data elements, formats and terminologies and develop methodological standards, to unlock the true potential of real world data

At Modis we strongly believe that RWD may substantially contribute to our knowledge of existing therapies and novel medicines under development. Modis has clinical project managers, data managers and clinical research assistants with hands-on experience in collecting and analysing RWD to support decision making.

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