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    Learn real-world evidence

    Go beyond the data — learn how RWE is being applied in oncology research and drug development.

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    The basics

    The articles and videos in this section cover the fundamentals of using real-world data (RWD) — where it comes from, how it is curated, processed and ultimately adopted for use by life science companies, regulators and other decision-making bodies throughout the oncology drug development lifecycle.

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      Key definitions

      Realizing the promise of real-world data requires alignment and consensus across many fronts, including the vocabulary of this emerging field.

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      What is machine learning's role in generating real-world data?

      Flatiron team members discuss the evolving role of machine learning and natural language processing (NLP) in real-world evidence along with appropriate applications for these technologies. Learn more about machine learning and AI.

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      How are real-world endpoints derived?

      Discover how real-world endpoints are enabling new approaches and how Flatiron is partnering with regulatory and industry stakeholders to develop and evaluate them. Learn about how real-world endpoints are derived in oncology

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      What's the latest FDA thinking on RWE?

      Chana Weinstock, MD, of the FDA discusses the use of real-world evidence in oncology drug approvals, the role of external control arms and how COVID-19 is impacting regulatory decision making. Watch her talk now.

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      An introduction to RWE for HTA and market access

      RWE is becoming a critical component of Health Technology Appraisals (HTAs) worldwide, particularly in oncology. However, questions remain about how it can be used effectively to support market access.

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      What does the future of evidence generation look like?

      Looking beyond real-world evidence, what does the future of evidence generation look like? Experts discuss the concept of integrated evidence and how it will power smarter care for every patient.

    Use cases

    Real-world evidence (RWE) is being increasingly used by life science companies to accelerate oncology R&D, help inform clinical trial design, fulfill post-marketing requirements and more. This collection of research, videos, case studies and more help to illustrate how your peers are adopting RWE.

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      Knowledge check

      Clinical trials can provide the most definitive and robust evidence for efficacy and safety. In 2021, the average oncology phase 3 study costs nearly 250 million dollars and takes more than 3 years to read out. Today, RWD is helping to improve trial designs decisions and accelerate drug development timelines. Read this collection to learn how researchers are using RWD to predict control arm performance, establish protocols according to the latest standards of care and more.

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      Real-world data and the case of the missing deaths

      Overall survival is a key endpoint in clinical trials. But when using RWD, how is missing data addressed?

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      Emulating control arms for cancer clinical trials using RWD

      This exploratory study assessed whether EHR-derived patient cohorts can emulate the control arms of published clinical trials that supported FDA approvals of anticancer therapies across multiple tumor types.

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      How rapid adoption of IO impacted outcomes in the real world

      Real-world data is curated to track novel therapies in real-time, when registries may lag for years. This makes it possible to anticipate and design trials based on actual current outcomes.

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      Use of a clinico-genomic database (CGDB) for R&D

      Combining real-world data to genomic datasets enables discovery of new prognostic biomarkers, and provides clinical evidence within unique populations needed for trial design.

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      Racial and ethnic disparities: What can we learn from RWE?

      In this episode, we examine racial and ethnic disparities through the lens of oncology, real-world data and health policy.

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      Statistical Biases in Real-World Data

      This article provides Flatiron research partners with a general overview of some of the common forms of statistical bias that can arise when working with RWD.

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      Differential frequency in imaging-based outcome measurement: Bias in real-world oncology comparative-effectiveness studies

      This study ultimately provided evidence that the frequency of imaging assessments to detect disease progression can differ by treatment type in real-world patients with cancer and researchers should remain cognizant of the potential influence of surveillance bias when working with RWD.


    As real-world data and real-world evidence continue to grow in adoption, the scientific community has increasingly discussed regulatory success and learnings, methodological considerations, data quality and endpoint validation. Explore events where RWE is on the agenda and join the conversation.

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