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Bias Is a Main Risk When Interpreting Real-World Data

Samara Rosenfeld
JULY 24, 2019


Real-world data are becoming more important for researchers and providers. Last December, the U.S. Food and Drug Administration (FDA) bet on real-world evidence and proposed a framework to advance the use of the data across drug and biologic development efforts.

But while the data could lead to more personalized treatments and therapies, they still present risks.

Andrew Norden, M.D., chief medical officer of Cota, told Inside Digital Health™ that one of the main risks is bias when interpreting real-world data.

For example, Norden said that if there is a study based on real-world data to compare patients who have been treated with two different types of drugs, each physician has a reason for making the choice of which drug they prescribed.

“And those reasons, critics fear, might not be well-captured in the data sets that we’re using to assess these questions,” he said. “That is a real issue and one that must be addressed.”

One way to address this is to generate a clinically granular data set to make sure the key information is extracted from electronic health records (EHRs). But some feel that there are likely unmeasured variables that can’t be extracted from EHR data, which Norden said is a limitation of using real-world data.

“And that’s why we have to be careful about how we apply this,” he said.

Another issue is that the data quality varies by provider and source and are not designed to answer certain questions. Providers should make sure they know that the data sources they’re looking at are well designed to answer the questions that they have.

It is not necessary to try to replace clinical trials either, he said.

“I think we can start by saying, ‘How do learnings from real-world evidence-based sources extend our understanding from clinical trials?” Norden said.

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