Sujay Jadhav, CEO at Verana Health, is advancing clinical trial capabilities, data-as-a-service offerings and data enrichment.
Clinical trials are the foundation of medical innovation, enabling life sciences companies to evaluate the safety and efficacy of new treatments. However, traditional study designs face increasing challenges, including recruitment barriers, ethical dilemmas and prolonged development timelines, delaying patient access to life-saving therapies.
As demand for more efficient and inclusive trials grows, alternative study models are emerging to optimize research without compromising scientific integrity. One such model is the external control arm (ECA), which leverages real-world data (RWD) to supplement or replace control groups, especially when enrolling patients in placebo arms is impractical or ethically complex.
While randomized controlled trials (RCTs) remain the gold standard in clinical research, integrating real-world evidence (RWE) through ECAs offers an opportunity to accelerate drug development and improve trial feasibility without compromising scientific validity. The growing availability of high-quality RWD has made ECAs a viable alternative to traditional control groups, enabling life sciences companies to streamline studies, expand access to innovative therapies and reduce patient burden.
Challenges Of Traditional Trials
Traditional clinical trials are becoming difficult to execute. Patient recruitment is a significant challenge, particularly for diseases with small or highly specific patient populations, such as rare diseases, oncology and ophthalmology. Enrolling patients in control groups can take months or even years, slowing the introduction of new treatments.
Ethical concerns further complicate trial design. In life-threatening conditions where no standard of care exists, placing patients in placebo groups raises ethical questions. Many patients are reluctant to join studies that may not guarantee access to potentially life-saving therapies.
Beyond these challenges, trials take years to complete due to data collection, analysis and regulatory review, which can prevent timely access to new treatments. Diversity in clinical trials is also an issue, as underrepresentation of racial, ethnic and geographic groups in trials can lead to gaps in treatment effectiveness.
How ECAs Help Solve Clinical Trial Challenges
ECAs can address these issues by using existing patient data. Instead of requiring newly recruited patients to participate in placebo arms, life sciences companies can analyze data from past patients with similar disease characteristics. In my experience working with this model, I’ve found this approach can accelerate trial timelines, improve feasibility, reduce burden on trial participants and reduce barriers to participation, increasing both diversity and efficiency in clinical trials.
Another potential benefit is reducing time-to-market for new therapies. By eliminating the need to recruit separate control groups, ECAs can allow studies to progress more quickly, bringing innovative treatments to patients faster, especially in diseases with no existing options. Additionally, ECAs can help ensure all participants receive active treatment by comparing therapies to historical patient data, rather than assigning patients to a placebo arm. This can help enhance study feasibility while maintaining scientific rigor.
Oncology And Ophthalmology: Case Studies In Action
The benefits of ECAs have already been demonstrated in several high-profile clinical trials. For example, Kymriah’s Phase II trial for follicular lymphoma lacked a traditional control arm but used an ECA with matched real-world oncology data. Researchers showed favorable outcomes with Kymriah compared to standard care, highlighting ECAs’ potential to complement or replace control arms and overcome ethical and operational RCT challenges.
However, ECAs are not without challenges. The BALVERSA trial, which investigated a treatment for metastatic bladder cancer, faced regulatory scrutiny due to missing data and inconsistent eligibility criteria. This highlights the need for well-matched designs and quality data to ensure scientific rigor and regulatory acceptance.
How RWD And RWE Enable The Use Of ECAs
The growing adoption of RWD and RWE has been instrumental in making ECAs a viable option in clinical research. RWD consists of healthcare data collected outside of traditional clinical trials, including electronic health records (EHRs), medical claims and genomics. RWE leverages this data to generate insights that inform regulatory and clinical decisions.
An advantage of using RWD in ECAs is its ability to enhance study design. With curated, high-quality datasets, life sciences companies can build control arms that closely reflect the characteristics of patients in traditional trial groups, which can help ensure scientifically valid and meaningful comparisons.
Regulatory agencies, including the FDA and EMA, recognize the value of RWE in clinical decision-making. The FDA released guidance on using RWE in drug development, paving the way for broader ECA adoption. As regulatory frameworks continue to evolve, ECAs should become an increasingly accepted component of clinical research.
Best Practices For Designing External Control Arms
Successful ECAs depend on rigorous planning and execution; cases like the BALVERSA trial highlight the importance of data integrity and methodological rigor. When developing an ECA, here are some best practices that can help ensure your ECA serves as a valid alternative to traditional control arms:
1. Feasibility And Strategic Alignment: When assessing whether an ECA fits a trial’s context, consider the availability and quality of RWD, the complexity of analysis and any regulatory expectations. Clearly articulate the rationale of the study to align goals and manage expectations early.
2. Data Source Selection And Validation: Selecting the right data source is critical. Whether you’re using EHRs or historical trial data, ensure relevance, completeness and sufficient detail. Prioritize sources that capture key variables and outcomes, and rigorously validate them to maintain data integrity and study alignment.
3. Study Design And Protocol Development: In my experience, a robust ECA requires a clear protocol and statistical plan. Define the inclusion criteria, analysis methods and sensitivity analyses upfront. Use matching strategies, such as propensity scores, to reduce confounding, and be sure to address biases (e.g., immortal time bias) and ensure population comparability to uphold scientific rigor.
Embracing ECAs For Innovation
As regulatory agencies increasingly recognize the value of ECAs, I believe their adoption will continue to grow. Advances in AI-driven analytics and data science should make ECAs even more accurate and reliable, further strengthening their role in drug development.
By integrating ECAs into trial design, the life sciences industry can accelerate approval of innovative therapies, broaden patient access and improve efficiency in clinical research. As medical science evolves, consider how incorporating ECAs might help your organization overcome the limitations of traditional trials and ensure patients receive timely access to needed treatments.
Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Read the full article here
