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37min

How Standardization and Consistency Help With Using Real World Data in Clinical Research


Summary

All data companies working in the healthcare industry are on a mission to help with the development of drugs and high-quality treatments while aiming for the best patient outcomes. 

But, unlike controlled conditions under which clinical trials are conducted, real world data is collected from a variety of different sources. It is an unregulated but critical area of clinical research. That’s why many conversations around the standardization of this type of data have occupied data scientists’ attention. The FDA has recently released a document with guidance on how to use real-world data for regulatory submissions.

In this episode of Real World Talk, our host Zoe Li is joined by her colleagues, CK Wang, Laura Fernandes, and Andrew Belli, to discuss the draft guidance on RWD recently released by the FDA . They emphasize that it’s still draft guidance, and it is yet to be seen in which direction it will go. However, they all agree that it is essential to establish standards regarding how companies collect, analyze, and use real-world data that will contribute to clinical research.

Highlights

  • [01:05] Introduction — In today’s episode of Real World Talk, our host Zoe Li welcomes CK Wang, COTA’s current CMO; Laura Fernandes, senior statistical director; and Andrew Belli, the VP of research and quality at COTA. 
  • [06:07] How to use real-world data for regulatory submissions — The FDA recently released a document with guidance focused on the use of real-world data, its sources and quality, and the influence it can have on all kinds of clinical research and approvals, not only on clinical trials. 
  • [13:17] Using the same standards for real-world and clinical trial data: Yes or No? — Laura explains that it is not a question of whether we should hold real world-data to the same standards as clinical trial data. The standard is about whether the approved drug is safe and efficient — so, the question of the standards should revolve around whether a drug does what it is supposed to do.  
  • [24:14] Standards are necessary but vary between events — According to our guests, data standardization is essential, but it is almost impossible to use the same set of standards for every piece of data that’s out there. ”There’ll be different standards based on different data sources. And what we need to do is be consistent in what we’re doing. So, at least, we can claim that when we see a particular event happening, this is how we have consistently done it for all the patients,” says Laura. 
  • [30:10] The guidance alludes to what kind of standards are expected — Although, as our guests emphasize, almost every company working on or having access to real-world data has developed its approach to collecting it, a certain level of standardization, especially from an operational standpoint, will benefit clinical research in general.
  • [35:04] It’s vital to really understand the need to document thoroughly, accurately, and completely — Finally, our guests agree that the quality of data is what we should especially focus on when looking at all the recommendations coming out of the guidance we discussed in this episode. So, in order to contribute to clinical research as well as drug development, quality of treatments, etc., healthcare providers, for instance, must rethink the ways they approach collecting and analyzing the data they hold.

Key Points

  • We need to make clinical trials more accessible to cancer patients. Dr. Gwen talks about the importance of clinical trials for developing effective drugs and blood cancer treatments. Despite the fact that trials are extremely beneficial, it is still a challenge for some patients to participate sometimes due to different factors. It’s up to those designing the clinical trials to think about the end patient more. “Does that test really need to be done? And does it have to be done only at a big academic medical center? Are there things we can do to make trials more accessible to more people?”
  • Real world data is important. When developing cancer treatments, we need to base them more on real-world data. Using real-world data can help ensure safer and more effective therapies. However, Dr. Gwen says clinical trials are often limited in terms of participants. “We’re hoping to change that, but in the meanwhile, drug developers, and I know because I’ve been one, their mind is set to say, ‘I need to move this drug so that it’s available to patients.’ The best way to do that is to choose patients who are going to tolerate the drug and will prove the point that the drug works. The problem is that’s not what the average patient is like.”
  • Predictions for the future. Some of Dr. Gwen’s predictions and hopes for the future include cancer prevention, possible early detection, and a better understanding of the immune system. Even though she wasn’t as hopeful about prevention before, she thinks it’s finally on the horizon, and we can expect to see major developments in cancer prevention in the near future. “We’re at the precipice of having some way to do early detection. Our next step is to then say, ‘What do we do to prevent that from becoming full-blown leukemia?’ And so, I think that prevention is on the horizon. It’s very early, but there’s some hope.”

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