Could real world evidence replace controlled experiment data?

By Melissa Fassbender contact

- Last updated on GMT

(Image: everythingpossible)
(Image: everythingpossible)

Related tags: Data, Real world data, RWE, Cytel

Cytel examines the gap between controlled experiment and real-world data at a time when the industry is collecting more data from more sources than ever before.

Cytel Inc., which provides analytical software and clinical research services, recently released a new report based on a survey of more than 140 industry respondents.

Caroline Morgan, DPhil, VP of strategic consulting at Cytel, said the survey confirmed that biopharma companies are already highly invested in data science at an organizational level. Per the report, three-quarters of respondents highlighted that their organization had a dedicated data science department.

“Today, data science approaches are being used across a broad range of data sources from social media data to electronic health records. Unsurprisingly, respondents cited historical clinical trial databases most frequently as a source,”​ said Morgan. 

Most surprising? Forty-four percent of respondents believe that in some cases, real-world evidence (RWE) could replace evidence generated by controlled experimentation for approval of a new drug.

Of the respondents who held this view, 80% predicted this could happen within the next decade. “Such a shift could revolutionize drug development, particularly in areas of high unmet medical need such as oncology,” said Morgan.

Conversely, other respondents said RWE would only serve to supplement controlled experimentation for regulatory approval.

Morgan said controlled experimentation remains central to generating evidence, though in circumstances where such evidence is difficult to obtain, “real world data will play a critical role,”​ she explained.

But what is data science?

While respondents cited investment in data sciences, less than 1 in 7 offered a definition of data science. 

For Cytel, Morgan cited David Donaho’s description in the publication 50 Years of Data Science: “Data Science is the science of learning from data; it studies the methods involved in the analysis and processing of data and proposes technology to improve methods in an evidence-based manner.”

“Naturally, as the field evolves and matures, so will the definition,”​ she added. “We strongly believe that such an evolution will help to unlock the full potential of these approaches for the benefit of society.”

By the numbers:

  • Less than 1 in 7 of all respondents suggested a definition of data science
  • Three-fourths of respondents said their organizations had a dedicated data science department
  • The majority agreed that improved clinical trial design is a goal that will benefit most from data science
  • Skill gaps were cited by almost half as a key barrier to greater use of data science
  • Nearly half believe the cost of standardizing and cleaning databases to be a challenge for the data science community
  • 44% of respondents believed that data science approaches on real-world evidence may in some cases replace controlled experimentation for the regulatory approval of a new drug

The benefits and challenges

Unlocking the potential of RWE to understand better disease patterns and patients outcomes was “overwhelmingly”​ the greatest perceived opportunity of data science, Morgan explained.

However, challenges remain. For example, Morgan said the question of trust is critical, “we must be able to protect patient privacy while exploiting the valuable insights that real-world patient data could offer.”

She added, “We want to support complex medical decision making while keeping medical professionals in the driver’s seat by providing transparent and controllable algorithms.”

As across many industries and roles, skill gaps also are another major barrier – and one that requires a collaboration to develop the workforces’ expertise in machine learning and artificial intelligence (AI).

Additionally, company silos that exist between analytical disciplines must be bridged, Morgan said.

She added, “The questions of how regulators would incorporate real-world evidence as part of the approval of a new product, and the confidence level that will be required in evidence generated by predictive analytics and AI need to be discussed and addressed.

“Yet, with a changing competitive and regulatory landscape, and access to expertise and technology, adaptive designs are becoming increasingly mainstream.”

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