How to Use Real-World Data to Develop External Control Arms for Clinical Trials?
The clinical trial sponsor companies have used real-world data (RWD) for years to generate evidence after their therapy is approved. Can they also use it to prove safety and efficacy during clinical trials?
Mounting evidence shows external control arms, which use data collected outside of the current trial, can augment traditional control groups when the standard approach isn’t feasible. In some cases, an external control arm may even replace the traditional control group.
To get the insights needed from both your internal and external controls, external data must integrate and harmonize with clinical data. Traditional point-solutions such as EDCs lack the capability to integrate diverse data sources and deliver the robust analytics needed for effective clinical research. Before we delve into clinical systems, let’s take a step back and explore why you would need an external control arm and the advantages of taking this route.
What is an
EXTERNAL CONTROL ARM?
An external control arm is a type of comparator group comprised of individuals who are not participants in your clinical trial. In this type of controlled trial, “the external control arm can be a group of people, treated or untreated, from an earlier time (historical control), or it can be a group of people, treated or untreated, during the same time period (concurrent control) but in another setting,” according to recent FDA draft guidance.
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Data for the external control arm can come from previous clinical trials or from RWD sources such as electronic health records (EHRs), patient registries, and medical claims.
When do I need an external control arm?
The FDA has long recognized the need for external controls when the traditional route isn’t feasible or safe for the patient. A few examples include:
Pediatric trials
Rare and ultra-rare disease studies
Oncology studies where using a placebo would be unethical
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Here’s an example of how AstraZeneca (AZ) used an external control arm. In 2020, the FDA approved AZ’s selumetinib for use in pediatric patients with inoperable plexiform neurofibromas, a common manifestation in the disease neurofibromatosis type one (NF1). In the Phase 2 SPRINT trial, researchers used an external control arm based on data from a previous natural history study of NF1 and a previous NF1 clinical trial.
In this case, the patient population was small. Recruiting patients for a placebo or comparator group would have added a lot of years to the clinical trial—time these little patients didn’t have. Reducing time to market was critical in this case. Given the nature of the patient population and the disease, the FDA allowed AZ to use an external control arm.
What are the BENEFITS of an external control arm?
Using external control arms offers the following benefits:
Most importantly, it offers the potential to bring more targeted therapies to more patients sooner. RWD allows biologics developers to study therapies in small, targeted patient populations more efficiently. Studies in patients with significant unmet needs become more feasible, thereby bringing effective treatments to patients who may not have other options.
How would you like to cut cost per participant in half?
When you rely on external data for a comparator group, you accelerate patient enrollment because you have fewer patients to recruit. You also dramatically reduce trial costs.
Using RWD as part of an external control arm also relieves some of the site burden. Sites collect data from fewer patients, which frees up staff to provide attentive, personalized care.
Patient costs can exceed $100,000/participant in an oncology trial
What are the CHALLENGES associated with using RWD for an external control arm?
Though external control arms based on RWD have the potential to decrease development costs and speed up time to regulatory approval, integrating data isn’t as easy as uploading a photo to your laptop. Primary challenges include the following:
LACK OF STANDARDIZATION
EHRs, payer systems, patient registries, and clinical technology systems like EDCs all follow different standards. Without a tool that integrates and harmonizes data, study teams spend weeks on data cleaning. In the first-to-market race, those weeks equate to millions to billions of dollars lost.
If you’re bringing in data from past clinical trials, data management becomes even more involved. Factors such as duration of the placebo arm or the number of patient dropouts in previous trials can affect the current analysis. If treatment guidelines changed between the previous and current trial, the old trial data may not be usable. The recent FDA draft guidance outlines other points to consider when comparing trial data.
PATIENT PRIVACY
Data from all these disparate sources must be de-identified to protect patient privacy. To do so typically requires a third-party tool that anonymizes data.
DATA BIAS
Several factors can lead to data bias, including mismatches in patient populations, incomplete data, selective reporting, and inconsistent measurement of outcomes. Developing an analytical approach in the design phase will help mitigate these issues. Your clinical technology could exacerbate data bias issues if it doesn’t harmonize data sources correctly.
How can biotech companies overcome roadblocks when developing external control arms?
When using external control arms, biotech companies must take steps to ensure they use only reliable, high-quality RWD. Considering external control arms early in protocol development will help mitigate quality and logistical issues later.
To integrate RWD and past clinical trial data, biotech companies have three options:
Piece together software from multiple vendors aka, a best-of-breed approach
Partner with a vendor that offers a purpose-built, end-to-end data analytics platform
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The first option is expensive and may involve hiring additional software developers and engineers. The second option introduces compatibility issues. APIs and middleware may help alleviate some of these issues.
The third option delivers the capabilities to support external control arms without the expense of hiring additional staff and without compatibility issues. They also provide several additional benefits.
Data Analytics Platform BENEFITS
DATA QUALITY
End-to-end data and analytics platforms can help ensure the quality and integrity of RWD. Data flows from external databases into your clinical system seamlessly, dramatically reducing or eliminating the need for time-consuming data entry or cleaning.
REAL-TIME ANALYTICS
End-to-end data and analytics platforms enable real-time analysis of RWD and other clinical data. Real-time availability helps you identify safety signals, supports patient monitoring, and informs clinical trial design.
Robust analytics also help mitigate data bias. For example, suppose an external control arm patient population doesn’t match the experimental group (varying disease severity or different standard of care, for example). Advanced technology can rapidly analyze datasets to accurately identify patient subgroups, which allows researchers to develop more accurate results.
INTEGRATION AND STANDARDIZATION
End-to-end data and analytics platforms integrate data from multiple sources, including eClinical systems, CRO systems, EHRs, claims data, and wearables. This gives you a harmonized view of the data to provide more comprehensive insights across the patient journey.
SCALABILITY
End-to-end data and analytics platforms enable efficient batch processing as well as storage and analysis of large volumes of data. These qualities are essential for leveraging large volumes of RWD in clinical development.
PREDICTIVE ANALYTICS
End-to-end data and analytics platforms enable predictive analytics. With these powerful insights, you can more easily identify patient subgroups, optimize treatment strategies, and inform personalized medicine approaches.
COLLABORATION
End-to-end data and analytics platforms facilitate collaboration between different stakeholders, including researchers, clinicians, and regulatory agencies. This happens because by way of automation, which helps establish role-based workflows, dashboards, and access controls.
MaxisIT's Clinical Data Analytics Platform leverages advanced algorithms and AI capabilities to automate manual, effort-intensive tasks to improve productivity and collaboration between stakeholders. It also offers real-time insight across the clinical and market access programs.
Because it is systems and data agnostic, MaxisIT’s platform integrates with most clinical systems. However, it may be the only technology you need to run your clinical trials—whether you choose an external control arm, a comparator group, or a traditional placebo group.
Optimized Clinical Trial Design
Insights into patient populations, disease patterns, and treatment outcomes.
Improved Oversight and Control over Regulatory Compliance
Ability to monitor and manage regulatory compliance by providing real-time insights into compliance status and potential risks before the issues become problematic.
Enhanced Operational Efficiency
Ability to identify operational inefficiencies and opportunities for process improvement, allowing to bring new therapies to market more quickly and at a lower cost.
Better Patient Outcome
Data about patient outcomes and treatment journey, allowing to develop more personalized treatment plans that can improve patient outcome and reduce healthcare costs.
About MaxisIT
MaxisIT’s purpose-fit and intelligent clinical data analytics platform helps improve clinical trial performance, mitigate risk, and optimize clinical outcomes. We provide a centralized and reliable source of truth for diverse data types from various sources, giving life sciences companies real-time insight to shorten cycle time and increase return on investment.
Incorporating an end-to-end clinical data pipeline from intake to visualization, MaxisIT's solutions are powered by AI/ML and metadata-centric approaches. Our impressive portfolio of over 3,300 clinical trials and an unparalleled 100% customer retention rate affirm the quality and reliability of our services.
Moulik Shah Founder & CEO, MaxisIT
Moulik Shah is a passionate healthcare technology entrepreneur and the visionary CEO of MaxisIT, where he has been at the forefront of leveraging technology to transform pharmaceutical and life sciences clinical trials.
His dedication to improving patient outcomes and his thought leadership in patient-centricity, patient diversity, interoperability, and real-world-data-led collaboration have been at the core of his vision of an integrated healthcare ecosystem based on effective use of data and analytics platforms.
He has been instrumental in driving innovation and progress in the industry. Under Moulik’s leadership, MaxisIT has become a leading provider of clinical data and analytics which is driving real-world impact in the pharmaceutical and life sciences clinical trials.
