BETA - What is real world data? How can real world data be used in disparity research?

What is real world data? How can real world data be used in disparity research? What is real world data and can we use real world data, which is difficult to say, to help answer some of these questions about the lower accrual of people of different ethnicities and backgrounds. So real world data is when a clinical trial is done in a small number or done at a center and based on that clinical trial that then drug gets to be available to the lay community. People aren't involved in clinical trials. So then we go back and we say, so how is this, you know, we tested this drug in 300 people in a phase two trial. So how does this drug work in the real world? And there are two ways real world studies are done. Real world studies are done. They're either done retrospectively where different, where people collect data that is submitted either to the FDA, to the drug company, or they collect data themselves and look back to see what happened to these individuals, you know, real world people. And some of them are smart and they're prospective where once the drug is released that they follow them a little bit longer over a longer period of time. So a phase four trial where a drug is already approved by the FDA, but how does it perform in the real world? And that is decentralized. So that isn't at the, because you're talking about much, much higher numbers of people and they're decentralized. So you're talking about that the community doctor is following their patient to see how these drugs perform. And of course, if you are doing studies outside some of the big institutions, then you're getting a higher, you know, a higher number of people of different ethnicities. So the real world studies do work for that purpose. So they do absolutely give us information about what happens to people of different ages, ethnicities, patients in rural communities, patients in African American communities, people in Latinx communities, much better than what the big institution studies do. The drawback is that it's limited. So can you do genomics in the real world? It's a really expensive and a bit more difficult to do because that needs to be centralized. Can you look at, you know, a number of different other biologic or therapeutic outcomes? Usually the real world studies are pretty limited in what they can do. And of course, when it's retrospective, then it's a bit more harder to interpret that when you're looking back and saying, well, what happened? It can always get a little bit clouded in people's pre-research perceptions of what they want the study to show versus a prospective study, which is a bit more clean. So does that help? Absolutely. So does doing real world studies help? Yes. But it's limitations, especially with myeloma, where it is being now so genomic driven and so research and basic science driven, it does have those limitations. That's a very good question. So real world data, I'm going to take a step back and say, think about a clinical trial. Clinical trials are an example of when we are utilizing new treatments to see if we can have a better effect and better outcomes for patients with that particular disease. So in the setting of multiple myeloma, if someone is on a clinical trial, there is a certain eligibility criteria for choosing the patient who is going to be on the clinical trial. So in essence, these are patients who are usually much stronger, much more fit in terms of their physical condition. And we want to see that we're not having untoward effects with the new medications that we're utilizing in the clinical trial. Now real world is, sorry, and in addition to that, for clinical trials, the medication that comes from clinical trials are the medications that ultimately will get approved down the line. And so when these medications are being used in a real world setting, it's being used for everyone. And so you don't really know all the effects of the outcomes because these patients in the real world are not in a clinical trial. So they're not necessarily fitting that eligibility criteria which was used in the setting of a clinical trial. And so you will find that a lot of these medications, some of the adverse effects or some of the benefits may not be fully elucidated until you actually gather the real world data. So real world is more important, I think, because you really want to know what is truly happening with this medication. Are we truly getting the outcomes that we expect? Are we getting better than we expect? Which is great. I'm being optimistic. But are we also seeing things that we didn't see in the clinical trials? For example, patients who may have maybe altered taste which we may not have picked up in the clinical trial with a particular medication, for example. So these are all things that are important. And if you gather the information from the real world, then you can kind of look into things a little bit more streamlined and identify maybe there are certain medications sometimes that might be better for a certain biological disease. So not all patients with multiple myeloma behave the same. The disease doesn't behave the same in every individual. So one set of patients may have much more benefit from a certain type of a regimen that we use to treat patients with myeloma. And you won't really necessarily see that from the clinical trial, but the real world data might give you that information.