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Summary

In the world of clinical trials, having a well-defined research question is key. And that’s where fit-for-purpose data sets come in. But what are the factors for defining research questions?

In this episode of the Real World Talk podcast, we get to hear from Seshamalini Srinivasan, Karla Feghali, Mandy Kelly, and Laura Fernandes. They talk about the criteria for defining research questions, the importance of fit-for-purpose data, and the other factors to consider in real-world data sources.

Highlights

• [00:37] Introduction — In this episode, we’re going to hear from Seshamalini Srinivasan, Karla Feghali, Mandy Kelly, and Laura Fernandes. They start by introducing themselves and explaining their roles at COTA and Deloitte. Mandy is the director of COTA’s life sciences team, while Laura is the senior statistical director at COTA, which specializes in abstracting and curating electronic health records, especially in the space of oncology. Karla is a senior manager at Deloitte and focuses on helping clients use data and analytics for drug development. Seshamalini leads product strategy for developing real-world data solutions at Deloitte.
• [03:35] Real-world data is increasingly being used for regulatory decision-making — Real-world data has come a long way. Not only does it help researchers give meaning to data, but it’s also considered for regulatory decisions. Seshamalini says, “Instead of just supplementing results from clinical trial data, now real-world data can support clinical efficiency and everything from labeling changes — which is very important.”
• [04:35] Fit-for-purpose data is essential for supporting clinical decision-making — Seshamalini Srinivasan explains why fit-for-purpose data sets are important and what it means to have fit-for-purpose data. She says, “Why should pharma care? It’s for all the reasons that I just talked about. Being independent from just relying on a clinical trial, having other sources of information that can be used for regulatory decision-making, which is a huge value for pharma, if you think about it. Everything from supporting label changes and designing clinical trials all the way through to showing clinical effectiveness.”
• [06:09] Other benefits of fit-for-purpose data — Karla goes on to explain other important uses of fit-for-purpose data in the ecosystem. She emphasizes the importance of context and concludes that it’s critical to know where the question is coming from in order to give the right answer to it.
• [08:13] Regulatory setting vs. market research — Laura explains some of the trade-offs between regulatory setting and market research.
• [10:40] Make sure that data is relevant, complete, and timely — Context makes all the difference in a research study, so you need to start out by defining your research question. To do that, you need to make sure your data is relevant, complete, and timely. Seshamalini explains each of these factors in detail.
• [14:35] In the world of clinical trials, having a well-defined research question is key — Laura dives into the factors for defining a research question. She says, “Before you conduct a clinical trial, for example, a relevant research question would be, ‘How would you characterize the effects of a particular intervention in the world of oncology?’ This could mean something like, ‘Does a particular therapy cause the tumor to shrink and/or disappear, and how do you measure this tumor shrinkage?’”
• [20:03] The trade-offs to look out for in the use of real-world data sources — Mandy says, “I think once we have our well-defined research question, it’s really prioritizing what are the most important variables for us to have. And as we start to lower down in priority, what are some of the trade-offs and considerations we can make there?”
• [22:05] The resist criteria — Laura explains the resist criteria. She says, “Looking at both the worlds of the clinical trials and of the real world setting, what I have come to realize is that the resist criteria is something that was, in my opinion, created as an objective measure of an intervention.”
• [25:29] Documentation is vital in oncology research — Seshamalini explains why it’s important to document everything carefully and clearly in the oncology area.
• [27:56] How do you address limitations and biases? — Laura gives an example to explain the limitations and biases in clinical research.
• [33:05] Race and ethnicity are becoming more important — Seshamalini talks about the industry trends and innovations around real-world data. She says ethnicity and race will play a huge role.
• [34:31] The concept of tokenization — Seshamalini says, “If a patient A is being enrolled in a trial, let’s tokenize patient A, meaning, in an anonymized fashion, capture all their information, and once the trial ends, they go about their life, and they are still generating data through real-world data sources.” Laura adds that tokenization is key because after a patient participates in a clinical trial, we don’t know what happens to them later.

Key Points

• Why is fit-for-purpose data important? There are many benefits of fit-for-purpose data. From regulatory decision-making and supporting label changes to designing clinical trials and showing clinical effectiveness, fit-for-purpose data is necessary for the pharmaceutical industry. It can help create better medication, improve patients’ lives, and develop better therapies.
• Defining a research question. A well-defined research question makes all the difference in clinical trials. To define it, you need to make sure your data is relevant, timely, and complete. Seshamalini says, “Once you have all of this in, now you come to identifying your data, and this is where the fit-for-purpose data comes into play. When you are defining, you want to make sure that the data is relevant, complete, and timely.”
• Tokenization could help with seeing the true effects of clinical trials. Tokenization is a very powerful and beneficial concept in clinical research. It helps track patients even after the trial is completed, so you can see the benefits of the drug in the long term.