Learn real-world evidence
Go beyond the data — learn how RWE is being applied in oncology research and drug development.
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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Glossary
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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Video
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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Video
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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Video
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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Video
How early adopters are applying RWE
Across organizations, researchers are looking to assess how RWE can support their drug development pipelines. Hear perspectives from early adopters across clinical development, regulatory and market access roles.
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Video
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.
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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Trial design
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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Article For Research Partners
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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Publication Summary For Research Partners
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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Publication Summary For Research Partners
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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Video For Research Partners
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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Podcast For Research Partners
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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Methodology For Research Partners
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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Publication Summary For Research Partners
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.
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Biomarker prioritization
Knowledge check
With rapid advancements and improved access to broad-based genomic sequencing, there is a growing volume of data that can provide researchers with a unique opportunity to tease out novel resistance mechanisms, co-mutations or other genomic drivers for outcomes. Pairing this genomic data with clinically deep, longitudinal clinical data allows researchers to make the critical connection between a genomic profile and patient outcomes. See how researchers are using RWD to support the development of novel targeted therapies in the age of precision oncology.
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Video For Research Partners
Prevalence of TMB-H and association with survival in patients with less common solid tumors
This talk is part of the ResearchX session: The role of real-world data in tumor-agnostic drug development.
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Video For Research Partners
STK11 and KEAP1 mutations as prognostic biomarkers in an observational real-world lung adenocarcinoma cohort
Combining RWD to genomic datasets can leverage the discovery of associations between different mutations. This study evaluated the prognosis of co-occurring STK11 and KEAP1 mutations in lung cancer.
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Article For Research Partners
Four ways biotechs can accelerate their pipeline using real-world data in 2021
RWD, and especially those linked to genomics and real-world outcomes information, can help to accelerate and de-risk drug development pipelines. See how.
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Tutorial For Research Partners
Defining Genomic Biomarker Positive and Negative Cohorts
This article reviews some of the basic considerations and caveats when building biomarker positive and/or negative cohorts with Flatiron RWD.
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Publication Summary For Research Partners
RAS amplification as a negative predictor of benefit from Anti-EGFR-containing therapy regimens in metastatic colorectal cancer
This study found that patients with RAS amplification without RAS mutations have poor outcomes on EGFRmAb.
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HTA and market access
Knowledge check
Real-world evidence generated from electronic health records has been shown to be more relevant, timely, and representative for health technology assessments (HTA) compared to evidence from clinical trials. Longitudinal EHR data is uniquely positioned to answer effectiveness questions that are core to HTA decision making. Explore how real-world data are being used for HTA & Market Access, including what the National Institute for Health and Care Excellence (NICE) considers the role of EHR-derived RWE for HTA decision-making to be.
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Article For Research Partners
Temporal considerations for Health Technology Assessments that use real-world data
How does calendar-time consideration play out in HTA-related comparative effectiveness studies? Take a journey to the fictional country of Zamunda to find out.
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Video For Research Partners
Can Early U.S. Adoption of Cancer Drugs Inform HTA Decision-Making?
Dr. Pall Jonsson from NICE presents research as part of the three-year research collaboration between Flatiron and NICE.
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Video For Research Partners
Using EHR-derived real-world data for HTA decision-making and market access
In this video, Dr. Scott Ramsey discusses the current evidence gaps in HTAs and how real-world data can address those gaps and Dr. Dominik Heinzmann walks attendees through a practical use case of RWE for HTAs.
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Publication Summary For Research Partners
Comparative Effectiveness with RWD?
By considering the strengths and limitations of an optimized and realized population, we establish how EHR data can be used to identify appropriate populations for indirect treatment comparisons when assessing the benefit of a new medicine.
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Video For Research Partners
Opportunities to use EHR-derived RWE to inform HTA decision-making
In this session, use cases demonstrating the value of using RWE in the HTA setting were showcased as well as a discussion on the role of RWE with NICE.
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Post-marketing requirements
Knowledge check
As FDA works to strike a balance between accelerated cancer therapy approvals and ensuring patient safety from unrecognized adverse events, life science companies are quickly incurring regulatory debt by way of post marketing requirements and commitments (PMRs/PMCs). This collection shows how real-world data can be utilized to fulfill PMRs and PMCs by seeing how approved therapies are affecting patients who were not eligible to participate in the pivotal studies due to demographics or comorbidities.
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Video For Research Partners
Characteristics and outcomes of real-world patients with MSI-H solid tumors treated with pembrolizumab
Real-world data linked to a genomic dataset can further inform us whether clinical outcomes in rare subtypes are similar in the real-world compared to within clinical trials.
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Publication Summary For Research Partners
One year later: Using RWE to look at PDL1 therapies following approval
RWE can inform us whether clinical outcomes after FDA approval are similar in the real-world compared to within clinical trials. This study looks at PDL1 outcomes after 1 year post-approval.
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Publication Summary For Research Partners
Organ dysfunction in the real world: Adv Melanoma + immune checkpoint inhibitors
EHR derived data can evaluate patient populations that may be excluded within a clinical trial setting. This study assessed the use of immunotherapy among patients with organ dysfunction
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Tutorial For Research Partners
Real-World Overall Survival (rwOS) Tutorial
Perhaps the most commonly performed time-to-event analysis on Flatiron’s RWD is overall survival (OS) analysis.This article provides conceptual instruction on OS analysis and guidance for how real-world OS should be estimated using Flatiron data.
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Label expansion
Knowledge check
As much as 71% of adult cancer patients are reported to have received at least one off-label treatment. This presents a ripe opportunity for life science organizations to look at real-world data to understand how their therapies are being used in routine clinical care. Unlike claims-based RWD which may not show a complete picture due to potential issues with off-label reimbursement, RWD derived from the electronic health record provides a window into off-label use.
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Video For Research Partners
Postmarket real world data perspectives: IBRANCE (palbociclib) males case
This talk is part of the ResearchX session: Applying RWE to Regulatory Applications.
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Video For Research Partners
Kadcyla (T-DM1) in HER2+ mBC: Label update on LVEF following a post approval commitment – based on RWD
Real-world data can evaluate clinical outcomes of patients who were excluded in trials due to organ dysfunction, which enhances our safety data, and can lead to label expansion.
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Publication Summary For Research Partners
Using real-world evidence for label expansion: Use of palbociclib in male breast cancer patients
Real-world data makes it possible to evaluate treatment outcomes of patients that were initially excluded in clinical trials. This study expanded use of palbociclib in male breast cancer patients.
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Tutorial For Research Partners
Real-World Time to Treatment Discontinuation (rwTTD) Tutorial
Given its longitudinal nature, Flatiron RWD is often used for various types of time-to-event analyses, including treatment discontinuation. This article provides conceptual instruction on time to treatment discontinuation (TTD) analysis and guidance for how real-world TTD should be estimated using Flatiron data.
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Events
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.
Past-event recaps
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2021
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2021
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2021
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2021
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2021
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2021
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2021
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2021
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2020
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2020
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2020
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2020
