Using real-world data from real-life patients


72141592What would happen if we could ask any type of scientific or clinical question about patients, and then go out and find the data to answer our questions? With "real-world data," we can do just that.

Real-world data is all medicinal product data that comes from real-life patients. In contrast, clinical trial data comes from the gold standard of clinical research: a double-blind randomized clinical trial (RCT).

Access to real-world data changes the paradigm in a major way. Big healthcare and patient data is messy, incomplete and diverse. But new analytical technologies now make it possible to extract safety and effectiveness data about the new medicinal product. Whereas with RCT, we collect the data first and then do the analysis according to a predefined plan.

The list of real-world data sources is long, but here are some examples: pragmatic clinical trials, health devices and wearables, social media information, health insurance claims records and electronic health records. Real-world data is nothing more than a rough mirror image of the patient health state (1). Together with accurate diagnostics and molecular information, real world data can narrow the gap between research and the clinical practice.

Back in September 2015, the Academy of Medical Sciences (UK) and the Association of the British Pharmaceutical Industry held a joint workshop on real world evidence. Participants discussed the potential contribution that real-world evidence could make to market-approval decisions for new medical products.

The most important issues described in the workshop are:

  • The need for standards in assessing evidence about a medicine using real-world data.
  • Data privacy considerations needed to manage patient data according to regulations such as the European Commission’s new General Data Protection Regulation.
  • Clinical trials are the best scientific and clinical “hard” evidence there is about a medicine. Real-world evidence should be seen as “strengthening” that evidence base. Sometimes RWD leads to better or more appropriately designed RCTs. These RCTs can have more relevant end-points or better quality-of-life parameters for the patients. This latter point is particularly important for diseases for which no real therapy exists, such as Duchenne Muscular Dystrophy, a debilitating disease that affects young boys.
  • Terminology surrounding different evidence types should be clearly defined and used consistently.
  • Core data standards, such as OMOP and CDISC, should be set so that collected data is reliable and robust.
  • New solutions to manage and analyze large amounts of observational data are available.

Real-world data for better medicines

Today, real-world data leads to more robust product safety assessments. Can pharmaceutical companies learn more about the therapy effectiveness using real-world data in the near future? I think they can.

Asking the question first and then finding the data seems simple, but has a very big effect on statistical methodology. Data scientists need the right tools and solutions to mine this information.  Dedicated and robust real-world data solutions, such as SAS Real-World Evidence, that have the right analytical and big data health IT technology inside, will make a huge difference in this drive to personalized medicine.

The better exploitation of real-world data is exactly what we'll see in the years to come thanks to our capabilities for visualizing and analyzing medical big data. Together with genomics and other diagnostic accurate information collected from a patient, real-world data will find greater acceptance in the drive towards the “ideal world” of personalized medicine.

To learn more, download this white paper: Making Real-World Evidence Real.

This post is the first in a three-part series about real-world data. The second part will deal with the regulatory agencies’ view on real-world data for accepting evidence on new medical products. The third entry will be about analytical and data challenges related to observational healthcare data. All of this is leading up to the launch of a new SAS software solution called SAS Real-World Evidence. More information coming soon.

(1) Dickson and Pfeiffer. Real-World Data in the Molecular Era—Finding the Reality in the Real World. Clinical Pharmacology & Therapeutics, Feb 2016 vol. 99 (2), p.186 – 197.





About Author

Mark Lambrecht

Dr. Mark Lambrecht, Director of the Global Health and Life Sciences Practice at SAS, joined SAS in 2005 and leads a senior team working for SAS’ healthcare and life sciences industry and organizations. His team is bringing SAS’ new HLS solutions to market and is constantly looking for innovation by identifying customer needs. He's also interested in data standards which enable the industry to share and reuse patient data to find new cures and for the benefit of mankind. Prior to joining SAS, Mark worked in the pharmaceutical industry and studied at the KU Leuven, Belgium and at Stanford University, USA. There, he worked as bioinformatics scientist specializing as a data scientist for high volumes of biological and genomics data using diverse technologies and approaches.

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