If you’ve been following this blog for a while, you’ll likely have noticed that we often refer to the potential of data analytics and data-driven...
What do we mean when we refer to “real-world data”, and how can it streamline drug development?
If you’ve taken a look at our website or have been following this blog for a while, you’ll have come across the phrase “real-world data” on a number of occasions. But how exactly is this concept different to “standard data”, and why is it so important to consider it during the research and development phase of drug development?
In a nutshell, real-world data are data derived from outside of a clinical trial setting. This could be anything from a post made on social media, to feedback or input given as patient-reported data in a digital companion app. It refers to data gathered in settings other than tightly-monitored experimental situations, such as randomized controlled trials. “Standard data”, on the other hand, are data that originate in some form of moderated or observed setting, such as a clinical trial.
Whilst standard data is obviously valid and of use, some argue that it might not be as accurate in determining patient opinion on its own as it would be when combined with real-world data obtained outside of observation. It would be unreasonable to suggest that all data gathered in a clinical trial setting is subject to observer bias – a form of systematic discrepancy of the truth during the process of observing and recording information for a study – but it might be argued that conclusions derived from clinical data can be supported or disproved based on real-world data.
Conversely, the idea that real-world data on its own is enough to make substantial alternations to a new drug or product candidate is unwise at best. Both methods of data collection have their positive and negative aspects, but when used together, they can provide unique and valuable insights backed up by medical data and patient opinion – resulting in a treatment or medication that is effective and recognises the preferences of the individual.
Of course, everybody is different, and one patient might have a completely different response to another. That being said, by using data analytics algorithms, pharmaceutical companies are able to spot patterns and anomalies in patient-reported data, which can – if necessary – justify the case for further investigation in certain circumstances.
Real-world data also has the potential to highlight any side effects that were not observed during the clinical trial process. This approach to drug development is gaining traction: more and more pharmaceutical companies are listening to the preferences of their customers, not just their needs. A biotech company might have created the most effective drug ever when it comes to treating a particular disease or condition – but if nobody wants to use it, then the developers are looking at a relatively bleak situation, both financially and reputationally.
Both standard data and real-world data are valid and reliable methods of determining a patient’s response to a particular treatment. By combining these two forces, research and development teams have the opportunity to really hear what patients are saying about their medication – resulting in an end product that is not only effective, but also offers a more personalized experience. Patients know how their body is responding to their course of treatment, and if improvements to the product candidate can be made simply by listening to their opinions, it’s definitely something worth paying attention to.