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DE&I

Topic

1rem

Progress made in measuring decentralized clinical trial use in patient recruitment and retention.

Replacing Anecdotal With Empirical Evidence On Impact of DCTs

Research from the Tufts Center for the Study of Drug Development’s PACT Consortium shows DCTs encourage higher participation across multiple demographic groups.

New Data Links Decentralized Clinical Trial Approaches to Improved Diversity in Patient Populations

Investigators find that most exclusion criteria in critical care randomized clinical trials are justifiable, but 60% include at least one poorly justified exclusion, most commonly affecting pregnant or lactating individuals.

Study Finds Many Randomized Clinical Trials Have Poorly Justified Exclusion Criteria

A focus on data and process excellence advances product development.

Five Predictions That Will Drive R&D Effectiveness in 2025

In this video interview, Rachael Fones, director, government & public affairs, IQVIA, highlights the use of real-world data in setting population goals.

Identifying Clinical Trial Diversity Goals With Imperfect Data

, Rachael Fones, director, government & public affairs, IQVIA, discussed the importance of diversity action plans in clinical trials. Fones emphasized the need for understanding the intent behind goal setting, the ability to stay updated with FDA guidelines, and the importance of a robust population assessment.

ACT: How can sponsors confidently set diversity goals for trials with imperfect data?

Setting goals and finding the data to do it—I mean real-world data, especially, has so much value, so much value to planning and is so important, and there's growing sources and value in the real-world data. At the end of the day, though, data on population goals is not something you just pull from a database. It's not something that will ever be perfect and exact to your population, so we have to remember, that real-world data provides great insights, but it informs, it doesn't necessarily answer what your goals should be.

One of the things we've seen in our conversations with sponsors, and also in the broader conversation among advocates and industry, and my peers across other organizations is there's consternation around the data; how do you find the population data? Goal setting based on real-world data, based on epidemiology data and public data sources, it's widely acknowledged that these data sources are imperfect, often not granular enough to get to the specifics of your intended use population. While this is something we need to continue to pursue and improve data sources and improve techniques, I think that it's important to realize that real-world data will inform your goals. It's not going to answer your goals. That needs to be that full exploration of the data in the context of the disease, in the context of your trial, but we also need to think about it's understandable that sponsors and individuals within sponsor teams may feel some consternation in moving forward with imperfect data. Setting a goal, committing to a number with a regulator is intimidating, especially if you're concerned you can't reach that goal. On top of that, you look at enrollment sub-goals and enrollment in a trial where you're already worried about getting that complete is also concerning. What we help sponsors do, and often do, is really talk about them understanding that this isn't a pass or fail exercise. It's not what is the answer from this the data that that is the perfect answer, and then how do we get there and what happens if we don't? It's really about understanding that population, understanding those characteristics, setting a goal that is meaningful in context of that disease burden and differences in outcomes; meaningful, but yet achievable, and that's where really understanding the real-world data in context of your use population. If it's pre-metastatic, but second-line therapy, that is a bit of a difference from all breast cancer, for example.

Again, real-world data informs, but it doesn't answer it. Diversity and that population is specific for your disease area and for the trial, so what really is important is the ability to look at different queries, understand what that story might be, what the rationale should be to setting those goals. Then, the final piece of this is we all need to realize there's going to need to be some flexibility in learning as we go along. We can think that population looks like what it is, but there may be some learnings as you go along that perhaps that genetic marker, that for which there wasn't a lot of real-world data, maybe it has an outsized effect, actually, maybe it makes your population more available. The goal here, if we think back to the goals of diversity overall, again, it's not just about increasing participation and access to the trials, though, very important. It is about learning and feeding back. If we learn that one genetic marker impacts one race differently or more prevalently, that not only helps you develop your drug better, but it helps advance the science, advance the practice of medicine, and advance new therapies. I think also the more that sponsors and all of us in drug development see where some differences do and do not come into play, that this whole effort will not seem so foreign. It will become an important part of drug development and pay that benefit that that upfront exploration yields better trials, faster trials even, and more commercial and healthcare success.

Videos

In this video interview, Rachael Fones, director, government & public affairs, IQVIA, discusses the importance of goal setting in the context of FDA’s latest Diversity Action Plan guidance. 

Informing Diversity Goals for Clinical Trials

ACT: What should trial sponsors consider in working to define and inform diversity goals for trials?

In this latest version of diversity plan guidance, or the Diversity Action Plan Guidance, the FDA has put goals front and center. They've emphasized goals and then how you're going to get there, and that's really important from the standpoint of that's what they need to know and they need to hear and it's important from an operational standpoint, because if you don't know where you're going with your trial and what you're aiming towards, you certainly can't get there. That said, I think sponsors really need to understand, and we all, as advocates for diversity and inclusion need to remember that it's not just a number. The goal is to better understand your population before you start the trial. That population assessment is so key, and it's one of those things that we found in doing diversity plans is true.

As someone who's done policy on this topic for years, we understand that the intent of this is not just to meet that number, it's to truly understand what the differences are. Is there a difference in effect? To start that off, you need to make sure you do that population assessment and one of the things is in that we found, is that thorough that assessment to truly understand what your population looks like, what differences are in the care journey that a different race or ethnicity may face, a difference in how they present, differences in outcomes. Those things are so critical to informing that goal setting because your goal-setting has to be in context of the population for your trial. It has to not just be, let's say, breast cancer at large. It needs to be that specific type of breast cancer and specific place in the treatment.

Again, the population assessment, what we've found is, not only is it where you can turn your goals into meaningful, yet achievable goals so you understand what that population looks like, but it's also where you uncover some of what I often call the true gold in this. If a population tends to present differently in one condition or another, that's an important nugget from the population assessment. It may not affect your goals, but it will affect your operational plans, and then it also affects your ability to be successful. For example, we've seen populations where perhaps—let's take the Hispanic ethnicity as a group, first of all, we all know that's a simple proxy for a very diverse population—but digging into the literature and understanding, we start to learn things that you may not have seen if you weren't looking for it. For instance, some Hispanic populations, depending on their country of origin, didn't have a high prevalence of disease, but the disease particularly affected the Mexican American population. That is what I call gold that is helpful for planning, for site selection, for informing investigators, maybe even informs how you do your recruitment materials, and informs perhaps your commercial strategy. These are things that prior to doing diversity guidance, we weren't looking at. You assume all people react the same, and you proceed with that in a clinical trial, but diversity planning and doing a population assessment up front is yielding some useful insights, not just for better goal setting, but for better planning and better commercial success.

Diversity Action Plans

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