Real-World Data Archives

Career Perspectives: A Conversation with Angie Raad-Faherty

In this latest edition of our Career Perspectives series, we had the pleasure of speaking with Angie Raad-Faherty, Director, EVA Health Economics. Angie shares her journey from a background in applied mathematics and biology to a career in health economics. In this interview, she shares emerging trends in health economics and RWA, expertise that will be essential for the future of drug development, the importance of mentorship, and much more.

Can you give us a little background on your career so far? What inspired you to pursue an education in applied mathematics and biology, and how did that lead to a career in health economics?

My career journey has really been shaped by a deep passion for both mathematics and biology. Even back in high school, I loved the logic and problem-solving side of math, but I was equally fascinated by biology, especially in understanding how diseases affect the human body.

After completing my undergraduate degree, I took a graduate course that focused on disease mathematical modeling. This experience was pivotal, as it introduced me to the concept of integrating mathematical techniques with biological applications. I realized that my skills in mathematics could be effectively applied to address complex health-related issues, leading me to the field of health economics. I feel really fortunate that my background in applied mathematics and biology allows me to look at health problems from both a quantitative and biological lens.

In your current role, you balance leadership, coaching, and hands-on research. How do you manage this mix, and is it important for your job satisfaction to stay involved in both areas?

In my current role, I’ve found a rhythm that really works for me — balancing leadership, coaching, and hands-on research. Clear communication and thoughtful delegation are key, but I also make it a point to stay close to the actual work. I think it’s really important to empower my team to take ownership of their projects, while also being available to guide and support them when needed. What I’ve realized is that staying involved in the hands-on side of things isn’t just good for the work, it’s important for me personally. It keeps me engaged, helps me stay up to date with what’s happening in the field, and allows me to contribute in a meaningful way. Plus, it helps create a collaborative atmosphere where people feel supported and encouraged to try new things. That balance between leading and doing is what makes my role fulfilling. It not only makes me a more effective leader, but also helps us deliver stronger results as a team.

What do you like best about your role, and about working at Cytel?

What I love about my role is the opportunity to make a real difference in patients’ lives. By supporting my clients, I’m able to contribute to bringing innovative, cutting-edge treatments to patients in need.

I also enjoy the unique challenges that come with each project — no two are ever the same. Every new project is a chance to learn something new, whether it’s about a different disease area or an emerging therapy.

And finally, one of the things I truly value about working at Cytel is the people. I get to engage with a variety of clients and work on diverse projects and indications, but just as importantly, I’m surrounded by incredibly smart, driven, and supportive colleagues. It makes the work both meaningful and enjoyable.

In your opinion, which skills are critical to a function as a research consultant at Cytel?

I believe both hard and soft skills are critical for a research consultant in the HEOR field in general and at Cytel. On the hard skills side, strong technical skills in HEOR methods, evidence synthesis, and understanding HTA requirements is essential — especially since expectations and requirements differ between HTA bodies.

Soft skills are just as important, like critical thinking, flexibility, and clear communication. In my experience working on HTA submissions for both countries, I learned it’s not just about building strong models but also explaining the results clearly to different audiences and adapting based on local needs.

Success really depends on balancing technical excellence with the ability to collaborate and adjust strategies based on specific client and agency expectations.

Given how quickly the field of health economics evolves, continuous learning is crucial. Are there any skills or areas of expertise you’re currently focusing on that you believe will be key to the future of drug development in 2025 and beyond?

Absolutely, continuous learning is not just beneficial, it’s essential in our field to stay ahead of the curve in areas that will define the next decade of drug development and market access. While technical skill development is ongoing, my focus is also on strategic foresight. Right now, I’m particularly focused on three key areas:

First, the integration of real-world evidence into economic models. We’re seeing increasing acceptance from HTA bodies to go beyond clinical trial data. As such, building rigorous frameworks for incorporating real-world data, while maintaining methodological transparency is a top priority.
Second, understanding how machine learning and artificial intelligence can be leveraged in health economics. There’s huge potential here, but it’s critical we align these innovations with HTA standards and ensure that models remain transparent, logically sound, and valid for reimbursement decisions.
Finally, I’m very focused on global HTA alignment. As frameworks become more interconnected, strategically aligning value messages and evidence packages across jurisdictions will be key to driving efficiency and access.

Are there any emerging trends in health economics and RWA that excite you right now?

Absolutely, there are several exciting developments in health economics that I find particularly inspiring.

One major trend is the increasing use of real-world data earlier in the drug development process to inform trial design, support regulatory decision-making, and identify unmet needs or specific patient populations.

I’m also really encouraged by the growing emphasis on patient-centered outcomes and health equity. There’s a broader recognition that value goes beyond traditional metrics like QALYs or ICERs. Incorporating factors like caregiver burden, and access disparities is making economic evaluations more holistic and aligned with real-world impact. Another area that particularly interests me is the use of surrogate outcomes. Being able to translate clinical endpoints into meaningful modeling endpoints is crucial, especially when long-term outcomes are difficult to measure directly.

Lastly, the advancement of AI and machine learning is a trend I’m closely following. These technologies are opening doors to deeper insights and faster analyses of complex, unstructured data. While we still need to ensure transparency and methodological rigor, the potential to uncover patterns and generate predictive insights is incredibly exciting. Overall, it’s a dynamic time in our field, and these trends are not only transforming how we work but also reinforcing the importance of continuous learning and adaptability.

Could you share a project you’ve worked on that you’re particularly proud of, and why?

Every project I’ve worked on in health economics has felt important to me, because each one represents a chance to help patients access innovative therapies. But the project that stands out most is actually the very first HTA submission I worked on early in my career. It was for early prostate cancer therapy. We were able to build a strong case for both the clinical value and the cost-effectiveness of the therapy, and seeing it approved and knowing it would change lives was incredibly powerful. That experience stayed with me and really shaped my passion for HEOR, showing me how our work can directly contribute to patient access and better outcomes.

As someone completing a PhD in applied mathematics and with a leadership role in RWA — both areas where women are underrepresented — what advice would you give to young women or girls aspiring to enter STEM fields?

STEM is full of tough questions and complex challenges — but that’s exactly what makes it so rewarding and interesting. Diverse thinking drives better science, more inclusive solutions, and ultimately, stronger outcomes in healthcare. And that’s exactly what our field needs. Also, mentorship and community are important. Throughout my journey, having people around me who believed in my potential, even when I didn’t yet see it myself, made a huge difference. That kind of support helps build the confidence not just to grow, but to lead. STEM needs more women not just participating, but shaping its future, and I encourage every young woman to see herself as part of that transformation.

Have you had a female mentor during your education or career? How did that impact you? Would you be open to mentoring women yourself in the future?

Yes, I had the privilege of working with my supervisor, Dr. Jane Heffernan, during my graduate studies. Her guidance and support were instrumental in shaping my academic and professional development. She provided valuable insights and encouragement, which significantly impacted my confidence and skills in my field.

In the future, I would be open to mentoring women myself, as I believe in the importance of supporting the next generation of professionals and fostering an inclusive environment in academia and beyond.

As a remote employee, how do you maintain a healthy work-life balance? What strategies work for you, and do you feel supported by Cytel in this regard?

To maintain a healthy work-life balance while working from home, I believe two key factors are essential. First, establishing a consistent routine with clearly defined working hours and scheduled short breaks throughout the day is crucial. This structure helps me stay productive while also allowing time to recharge. Second, creating a designated workspace that is separate from my everyday home activities is vital. This physical distinction not only enhances my focus but also aids in mentally transitioning between my professional and personal life. I appreciate that Cytel supports this balance through its flexible working hours, which further enables me to manage my responsibilities effectively.

What are your main interests outside of work?

Outside of work, I really enjoy getting outside for walks and hikes with my dogs — it’s one of my favorite ways to unwind. I also love spending time in the kitchen, whether I’m baking something sweet or trying out a new dish from a different cuisine. It’s my way of relaxing and getting a little creative.

Finally, what’s one piece of career advice you wish you had received earlier?

A key piece of career advice that holds significant value is to prioritize networking and relationship-building within your industry. This includes not only connecting with colleagues beyond your immediate team but also engaging with clients. Cultivating a robust professional network can unlock new opportunities, provide essential support, and offer valuable insights that can greatly influence your career path. Additionally, actively engaging with mentors and peers fosters continuous learning and personal growth, making networking an essential component of career development that is often underestimated.

Advancing Equity in Health Technology Assessment: Lessons from CAR T-Cell Therapies

Chimeric antigen receptor (CAR) T-cell therapies, classified as advanced therapy medicine products, have revolutionized the treatment landscape for certain hematological cancers, providing new hope to patients who previously had limited options. Since the U.S. FDA approved tisagenlecleucel (Kymriah) and axicabtagene ciloleucel (Yescarta) in 2017 for relapsed or refractory B-cell precursor acute lymphoblastic leukemia and large B-cell lymphoma, respectively, evidence has suggested that CAR T-cell therapies could offer a potentially curative approach in a range of other hematological conditions.1,2,3,4

However, despite their potential to improve patient outcomes, access to CAR T-cell therapies remains inconsistent due to cost, delivery complexity, and manufacturing challenges. Additionally, disparities in access related to social determinants of health (SDOH) further limit equitable benefits, disproportionately impacting marginalized populations (such as those living in rural areas, individuals with no family or social networks, and older people).

Health technology assessment (HTA) has traditionally focused on clinical outcomes and cost-effectiveness. Although health equity has been recognized as a distinct value element in HTA, and relevant frameworks and guidelines exist, it is not routinely integrated into decision-making. As such, CAR T-cell therapies represent a valuable case study for better understanding and advancing equity considerations in HTA.

What are CAR T-cell therapies?

CAR T-cell therapies are a type of immunotherapy that modify a patient’s T-cells to target and attack cancer cells, offering effective options for relapsed or refractory hematological cancers. This process involves extracting, modifying, and reinfusing the cells, followed by close monitoring for severe adverse events. Beyond their current approved indications, CAR T-cell therapies are also being investigated for several other hematological malignancies, as well as in solid tumors and non-cancer indications such as autoimmune conditions.4

Delivering CAR T-cell therapies presents significant challenges for healthcare systems due to their complexity, high cost, and the need for specialized infrastructure and expertise. The treatment requires apheresis, cell manufacturing, conditioning therapy, and intensive post-infusion monitoring, all conducted at accredited centers, often located in major urban areas.5 Successful delivery also requires coordination among a multidisciplinary team of physicians, nurses, and pharmacists, along with investment in treatment center infrastructure, including intensive care unit capacity and specialized training to manage severe adverse events (e.g., cytokine release syndrome and neurotoxicity).5

CAR T-cell therapies: Highlighting equity concerns in access to innovative treatments

Ensuring that equitable access to healthcare is considered in the HTA decision-making, particularly for high-cost, innovative treatments like CAR T-cell therapy, has become a growing concern. Despite advancements in science, therapeutic applications, and complication management, access to CAR T-cell therapy remains limited, with only a small percentage of eligible patients receiving treatment.6,7 This restricted access stems from challenges specific to CAR T-cell therapy, such as high costs, complex logistics, and manufacturing constraints, which are compounded by factors related to SDOH and equity.

Equity gaps are evident in disease incidence and prevalence, treatment patterns, and outcomes of patients eligible for CAR T-cell therapies. For example, racial and ethnic minorities, particularly Black and Hispanic populations, experience higher rates of certain hematological malignancies, yet are underrepresented in clinical trials that inform CAR T-cell therapy approvals.8,9 This leads to gaps in effectiveness and safety data across populations. Furthermore, differences in diagnosis and referral patterns contribute to inequities, with marginalized groups less likely to be referred to specialized centers due to limited provider awareness or implicit biases. Older adults, who could benefit from CAR T-cell therapies, are often excluded from trials, limiting evidence for their use in this population.10 SDOH, such as geographic remoteness and socioeconomic status, exacerbate inequities in access to CAR T-cell therapies once they are approved. Patients living in rural areas face logistical and financial barriers to reaching treatment centers, while individuals from lower socioeconomic backgrounds struggle with transportation, caregiving responsibilities, and lost wages.11,12 These overlapping disparities create a cumulative burden, limiting equitable access and worsening outcomes for historically underserved groups.

Exploring equity factors in HTAs of CAR T-cell therapies and the journey toward inclusive access

Traditional HTA frameworks have historically overlooked equity considerations, prioritizing clinical efficacy and cost-effectiveness while neglecting how SDOH and equity factors affect patient access and outcomes. This gap not only exacerbates disparities but also fails to incentivize health technology developers to commit to systematic evidence gathering and addressing these issues in their evidence submissions. While several modified economic modeling approaches that account for equity considerations exist (e.g., distributional cost-effectiveness analyses, equity-based weighting, multi-criteria decision analysis), there is a lack of consensus on which approach is best and how these methods can systematically be incorporated into HTA.13,14 As a result, HTAs often do not account for the unique burdens faced by underserved populations, such as indirect costs related to travel, caregiving, and lost income, further exacerbating existing inequities.

Recent commitments to equity from HTA bodies present valuable opportunities to ensure fair access to novel, high-cost therapies.15,16 CAR T-cell therapies, with their complex delivery and high cost, serve as a compelling case study for examining how HTA bodies incorporate equity considerations into their assessments. To explore this further, we conducted a review of 18 HTAs from Canada’s Drug Agency and the National Institute for Health and Care Excellence, focusing on six CAR T-cell therapies. Our review found that most submissions acknowledged disparities in disease incidence, treatment, and outcomes based on race, socioeconomic status, diagnosis and referral patterns, and age. These disparities were often linked to financial and geographical barriers that disproportionately affect marginalized groups. However, there were limited and inconsistent efforts to quantify these factors in the economic modeling or in the analysis of the clinical evidence submitted. This likely reflects the fact that HTA bodies do not routinely require sponsors to quantify equity concerns within their submissions, leading both decision-makers and companies to potentially overlook these issues.

Cytel will present the results of this review at the 2025 ISPOR conference in Montreal, Canada, where we will explore how gaps in HTA evaluations can inadvertently perpetuate inequities in access to CAR T-cell therapies. Join us at our podium session to learn more about how incorporating equity considerations into HTA processes can promote more equitable outcomes and ensure that all patients, regardless of their background, can benefit from CAR T-cell therapies. Do not miss this opportunity to engage in the discussion on advancing inclusive access to high-cost, innovative therapies.

Addressing equity concerns in CAR T-cell therapies: Strategies for inclusive access

Cytel can support pharmaceutical clients in addressing equity concerns through the following offerings:

Innovative trial designs that consider elements of health equity
Generation of real-world evidence to supplement trial programs
Lifecycle evidence generation to support value in diverse groups of patients
Advanced analytics, such as transportability analyses, to maximize the use of evidence generated in other settings
Quantifying the impact of inequalities in the value proposition of new health technologies.

Getting Your Data Strategy Right: Seven Tips for Balancing Science, Efficiency, and Patient Centricity

In today’s clinical trial landscape, the sheer volume of data collected is both a blessing and a curse. While advances in data collection and analysis offer unprecedented insights into drug development, they also bring logistical challenges, increasing costs, and burdens on patients and research sites.

In the coming year and beyond, an effective approach to data will be more and more critical. Clinical research organizations (CROs) and sponsors must craft data strategies that are not only scientifically robust but also operationally efficient and patient-centric.

Here, we explore how to get your data strategy right by focusing on key principles and practical approaches that balance scientific objectives, operational realities, and participant well-being.

1. Define clear objectives: Focus on what matters most

An effective data strategy starts with clarity about what the trial is designed to achieve. The endpoints — whether efficacy, safety, or exploratory — should drive every decision about data collection. Too often, protocols become bloated with “just in case” data points, which can increase complexity without adding meaningful insights.

Prioritize critical endpoints: Identify and align on the primary and secondary endpoints that are essential for regulatory approval and decision-making.
Stakeholder collaboration: Work closely with sponsors, regulators, patient advocacy groups, and key stakeholders to define the minimum viable dataset required for success.
Eliminate non-essential data: Conduct feasibility assessments to identify redundant or low-value data points and exclude them from the protocol.

By narrowing the focus to critical data, you can reduce trial complexity, improve operational efficiency, and ease the burden on sites and patients.

2. Streamline safety data collection

Safety monitoring is a cornerstone of clinical trials, but it is also one of the most resource-intensive components. Collecting excessive safety data can overwhelm both sites and patients, delaying timelines and inflating costs. However, reducing safety data collection must be done carefully to ensure participant well-being is not compromised.

Timing and frequency: Align safety assessments with the drug’s pharmacokinetics and expected adverse event timelines to avoid unnecessary data collection.
Remote monitoring: Wearable devices, mobile apps, and telemedicine can be used to collect safety data in real time, reducing the need for site visits.
Simplify reporting: Limit detailed reporting to serious adverse events (SAEs) and high-priority concerns while streamlining processes for common, low-severity events.

By leveraging these approaches, trials can maintain high safety standards while reducing unnecessary data collection and operational overhead.

3. Optimize operational feasibility

Even the most scientifically sound protocol can fail if it is operationally impractical. Clinical trial designs must account for the practicality at research sites and the realities of the patient participation.

Site workload: Avoid overwhelming sites by simplifying data collection processes and limiting unnecessary assessments.
Patient-centric protocols: Minimize the burden on participants by reducing visit frequency, consolidating procedures, and using remote or decentralized trial models.
Stakeholder input: Engage site investigators and patients during protocol development to identify pain points and refine processes before trial launch.

Operational feasibility isn’t just about reducing site and patient burden; it’s also critical for ensuring data quality. Overly complex protocols can lead to errors, incomplete datasets, and costly delays.

4. Leverage real-world evidence (RWE)

Real-world evidence offers a powerful way to supplement trial data and reduce the need for redundant or duplicative collection. By tapping into existing data sources, such as electronic health records (EHRs), claims databases, and patient registries, CROs can streamline trial operations while gaining valuable insights.

Historical Comparisons: Use RWE to establish baseline safety and efficacy data, reducing the need for extensive data collection in the trial itself.
Synthetic control arms: Replace traditional placebo or control groups with synthetic arms derived from RWE, reducing the number of participants required.
Patient stratification: Leverage RWE to refine inclusion and exclusion criteria, ensuring trials target the right populations from the outset.

When integrated thoughtfully, RWE can significantly enhance efficiency while maintaining scientific rigor.

5. Harness technology for smarter data collection

Digital tools and advanced analytics are transforming how data is collected, managed, and analyzed in clinical trials. These innovations can help streamline processes, reduce redundancies, and improve data quality.

AI and machine learning: Apply predictive algorithms to identify critical data points and flag potential safety concerns, reducing the reliance on exhaustive datasets.
Decentralized trials: Implement decentralized models that allow participants to complete assessments remotely, improving accessibility and reducing dropout rates.
Wearable devices: Collect real-time physiological data through wearables, reducing the need for manual measurements and frequent site visits.

The right technology can make data collection more efficient while enhancing patient convenience and trial outcomes.

6. Engage regulators early

Regulatory expectations often drive the scope of data collection in clinical trials. Engaging with regulators early in the design process can help ensure that your data strategy meets compliance requirements without unnecessary over-collection.

Regulatory guidance: Familiarize yourself with evolving guidance, such as FDA’s initiatives on patient-focused drug development and real-world data.
Pre-submission meetings: Use pre-submission meetings to discuss and align on the minimum data required for approval.
Streamline post-market plans: Shift exploratory safety and efficacy data collection to post-market surveillance or Phase IV trials where appropriate.

By aligning with regulators upfront, sponsors can avoid unnecessary rework and streamline approval timelines.

7. Analyze and learn from past trials

Every completed trial offers a wealth of information about what worked and what didn’t. By analyzing past protocols, sponsors can refine their data strategies and avoid repeating mistakes.

Post-trial reviews: Identify data points that were collected but not used in analysis and eliminate them from future designs.
Feedback loops: Create systems for gathering feedback from sites, patients, and operational teams to inform future trial strategies.
Benchmarking: Compare your trial performance against industry benchmarks to identify areas for improvement.

Learning from experience and continuous improvement is key to optimizing data strategies over time.

Final takeaways

Getting your data strategy right is about finding the sweet spot between collecting enough data to meet scientific and regulatory goals and avoiding the pitfalls of over-collection. By focusing on clear objectives, leveraging technology and RWE, streamlining safety data, and designing trials with operational feasibility and patient needs in mind, sponsors and CROs can achieve this balance.

As the clinical trial landscape continues to evolve, a thoughtful, optimized, and patient-focused data strategy will be essential for success. By prioritizing efficiency without compromising quality, the industry can deliver better results — for sponsors, sites, and, most importantly, patients.

The Future of Drug Development: Data Science, AI, and the Evolution of the Clinical Trial

The year 2025 is poised to be a turning point in clinical development, driven by a convergence of trends that are reshaping the way we generate evidence and bring new treatments to patients.

A key theme emerging from industry discussions is the need to modernize the clinical trial process by embracing the power of data science, advanced analytics, and emerging technologies. While the traditional RCT remains a cornerstone of drug development, there’s growing recognition that it can be enhanced and augmented to meet the demands of the 21st century.

Several factors are driving this shift. First, the sheer volume and variety of data available to researchers is exploding. We are awash in data from electronic health records, genomic databases, wearable sensors, and even social media platforms. This data deluge presents both opportunities and challenges, requiring new tools and techniques to extract meaningful insights.

Second, the regulatory and reimbursement landscape is evolving rapidly. Payers are increasingly demanding evidence of value in parallel with regulators, with both encouraging the use of real-world data (RWD) to support submissions. In Europe, for example, the EU Joint Clinical Assessment (JCA) is creating common standards to expedite the HTA process and requiring sponsors to consider payer perspectives much earlier in the development process.

Third, the rise of precision medicine and targeted therapies requires more sophisticated trial designs to identify patient subpopulations most likely to benefit from treatment. Adaptive designs, master protocols, and the use of biomarkers as surrogate endpoints are all gaining traction.

Here’s a glimpse of what we might see in 2025

AI and machine learning will play an even more prominent role across the entire clinical development lifecycle.
- Algorithms will be used to identify promising drug targets, screen compounds, and optimize lead candidates.
- AI-powered tools will automate routine tasks in data management, statistical programming, and medical writing, freeing up researchers to focus on higher-value activities.
- AI will also power advanced analytics, enabling the development of predictive models that can forecast trial outcomes, personalize treatment decisions, and identify safety signals.

Simulation-guided design will become the norm. Sophisticated platforms will enable sponsors to evaluate a wider range of trial design options in silico, optimize resource allocation, and improve hypothesis generation.

The lines between different data sources will continue to blur. RWD will be routinely integrated with clinical trial data, requiring new statistical approaches like causal inference and quantitative bias analysis to address issues of bias and confounding.

Digital endpoints and biomarkers will move to the forefront. Wearable sensors, imaging technologies, and “omics” data will provide richer and more patient-centric insights into disease progression and treatment response.

One of the most exciting areas of innovation is the emergence of agentic AI and the use of digital twins and synthetic data.

These technologies have the potential to revolutionize clinical trials by:

Automating key trial processes: Agentic AI systems, trained on vast amounts of data, could manage tasks such as patient recruitment, data collection, and safety monitoring, potentially reducing costs and accelerating timelines.

Creating virtual patient populations: Digital twins, virtual representations of real patients built using diverse data sources, could be used to simulate the effects of different treatments, optimize trial designs, and even identify new drug targets.

Enhancing control arms: Synthetic data, generated by algorithms trained on real patient data, could be used to create external control arms, reducing the need to recruit control patients and potentially making trials more efficient and ethical.

The convergence of these trends will require a collaboration of clinical data scientists

— ones who not only master statistical techniques but are also fluent in data science, machine learning, epidemiology, and domain-specific knowledge of drug development. These individuals will be key to unlocking the full potential of the data revolution, translating complex insights into actionable strategies, and guiding the industry toward a future of more efficient, patient-centric, and data-driven clinical trials.

However, as we embrace these powerful new technologies, we must also be mindful of the ethical implications. Ensuring algorithmic accountability, transparency, and fairness will be paramount. The role of statisticians and data scientists will be crucial in guiding the responsible use of AI and ensuring that it benefits patients and society as a whole.

The year 2025 promises to be a pivotal year in the evolution of clinical development. By embracing innovation and collaboration, we can harness the power of data to accelerate the development of new treatments and improve the lives of patients worldwide.

Maximizing the Potential of Real-World Data with Bayesian Borrowing

In response to concerns about data quality in real-world evidence (RWE) generation, including issues such as bias and small sample sizes, resulting in low precision estimates with questionable accuracy and thus interpretability challenges, regulatory submissions have increasingly incorporated advanced methodologies to enhance the robustness of RWE.

Among these methods, Bayesian borrowing stands out as an approach that can significantly increase the scientific potential of real-world data. By leveraging data from multiple sources that may all have different weaknesses, Bayesian borrowing can combine these and enhance the power of comparisons with trial data for comparisons beyond those from a randomized control trial. Bayesian borrowing can also be used to create hybrid control arms, enabling a smaller control cohort to address ethical concerns and patient availability issues.1

The Bayesian borrowing concept

Bayesian borrowing methods make use of external data, potentially from multiple sources, by using a prior distribution that adjusts for the possibility that this external data may come from a different population. While using external or historical data can enhance the precision and accuracy of parameter estimates in a study, directly simple pooling of this data could lead to bias if the external population differs from the current one.2,3,4 To address this, priors such as a power prior is used to adjust the influence of the external data, which is more diffuse than complete pooling of current study dataset and the external dataset, reducing the possible bias but also the eventual precision of the parameter estimate.

In drug development, Bayesian borrowing is primarily applied in situations involving rare diseases, pediatric trials, or when there are no existing approved treatments for the same conditions.5

Figure 1. Bayesian borrowing

Quantitative bias analysis (QBA) plays a crucial role in supporting studies that employ Bayesian borrowing by assessing the impact that the weaknesses in the data being integrated has on study results. When leveraging external or historical data through Bayesian methods, such as Bayesian borrowing, there is always a risk that the borrowed data may introduce bias due to elements that cannot be addressed directly in analysis specifications, such as missing or unmeasured data, or other quality issues. QBA helps to quantify the extent of these biases and provides a structured approach to adjust for them, thereby enhancing the interpretation possibilities of the results, ultimately supporting study validity and scientific integrity.

By applying QBA alongside Bayesian borrowing, researchers can transparently account for uncertainties in the borrowed data and ensure that the final estimates are more robust, credible, and defensible in both regulatory and clinical decision-making contexts.

Figure 2. Example of QBA for Bayesian borrowing

FDA and HTA submissions incorporated with Bayesian borrowing methods

In recent years, the acceptance of Bayesian borrowing approaches has been evolving from both regulatory and Health Technology Assessment (HTA) perspectives.

The FDA has highlighted this shift through initiatives like a podcast discussing the use of Bayesian statistics, including a case where Bayesian methods were used to borrow data from an adult trial to assess an asthma product’s treatment effects in pediatric patients.6 Additionally, the FDA recommended that GSK apply Bayesian dynamic borrowing to integrate adult trial data for a pediatric study for post-marketing activities, and these results were subsequently accepted.7

HTA bodies are also considering Bayesian methods; for example, NICE recommended using Bayesian hierarchical models, which are closely related to Bayesian borrowing, in the technical appraisal of larotrectinib for NTRK-fusion positive solid tumors in 2020.8

Furthermore, the FDA plans to release draft guidance on the use of Bayesian methods in clinical trials for drugs and biologics by the end of 2025.

The future of Bayesian borrowing

Although Bayesian methods have garnered increasing attention from regulatory and HTA bodies, their practical implementation has been somewhat limited. Challenges such as organizational resistance to novel approaches, resource constraints, and difficulties in applying these advanced methods effectively can hinder their adoption in regulatory and HTA submissions. However, as awareness grows and best practices are established, these barriers are likely to diminish, paving the way for more widespread use of Bayesian methods.

Notes

1 Dron, L., Golchi, S., Hsu, G., & Thorlund, K. (2019). Minimizing Control Group Allocation in Randomized Trials Using Dynamic Borrowing of External Control Data – An Application to Second Line Therapy for Non-Small Cell Lung Cancer. Contemporary Clinical Trials Communications, 16(1).

2 Viele, K., Berry, S., Neuenschwander, B., Amzal, B., Chen, F., Enas, N., Hobbs, B., Ibrahim, J. G., Kinnersley, N., Lindborg, S., Micallef, S., Roychoudhury, S., & Thompson, L. (2013). Use of Historical Control Data for Assessing Treatment Effects in Clinical Trials. Pharmaceutical Statistics, 13(1).

3 Struebing, A., McKibbon, C., Ruan, H., Mackay, E., Dennis, N., Velummailum, R., He, P., Tanaka, Y., Xiong, Y., Springford, A., & Rosenlund, M. (2024). Augmenting External Control Arms Using Bayesian Borrowing: A Case Study in First-Line Non-Small Cell Lung Cancer. Journal of Comparative Effectiveness Research, 13(5).

4 Mackay, E. K. & Springford, A. (2023). Evaluating Treatments in Rare Indications Warrants a Bayesian Approach. Frontiers in Pharmacology, 14(1).

5 Muehlemann, N., Zhou, T., Mukherjee, R., Hossain, M. I., Roychoudhury, S., & Russek‑Cohen, E. (2023). A Tutorial on Modern Bayesian Methods in Clinical Trials. Therapeutic Innovation & Regulatory Science, 57(1).

6 Clark, J. (2023). Using Bayesian Statistical Approaches to Advance our Ability to Evaluate Drug Products. CDER Small Business and Industry Assistance Chronicles, U.S. FDA.

7 Best, N., Price, R. G., Pouliquen, I. J., & Keene, O. N. (2021). Assessing Efficacy in Important Subgroups in Confirmatory Trials: An Example Using Bayesian Dynamic Borrowing. Pharmaceutical Statistics, 20(1).

8 NICE. (2020). Appraisal Consultation Document: Larotrectinib for Treating NTRK Fusion-Positive Solid Tumours.

External Validity Bias in HTA Submissions: A Case for Transportability Methods

Health technology assessment (HTA) bodies support decision-making for the reimbursement of new technologies at the local or national level. Recommendations made by HTA bodies are based on various sources of evidence, ranging from the preferred standard randomized clinical trials to real-world data (RWD) when trials are unavailable or not relevant to the target population of the decision problem. Non-randomized studies of treatment effects are already widely used in rare diseases and innovative technologies to contextualize findings from single-arm trials. Watch our recent webinar on real-world external control arms here.

To build trust in the evidence that supports decision making, researchers need to understand and address potential risks to study validity.

Behind the Oncology Research: An Interview Between Robert Szulkin and Jana de Boniface

Welcome to our interview with Dr. Jana de Boniface, a renowned surgeon and researcher specializing in breast cancer. In this conversation, we delve into her inspiring journey, groundbreaking research, and collaborative efforts that have led to significant advancements in breast cancer treatment.

Robert Szulkin (RS): Jana, you’ve just published a paper on “Omitting Axillary Dissection in Breast Cancer with Sentinel-Node Metastases” in the New England Journal of Medicine (NEJM) [1]. Congratulations! This project was completed in cooperation with Cytel’s Real-World Evidence team, and we’d love to discuss the process that made this project a reality. But first, a brief introduction. You are a surgeon specializing in oncoplastic breast surgery as well as a Professor in the Department of Medical Epidemiology and Biostatistics at the Karolinska Institute in Sweden. I’d like to ask a bit about your background.
What inspired you to become a scientist and a surgeon?

Jana de Boniface (JdB): I began my journey as a scientist during my university years in Berlin, and that was just a completely different topic, it was psycho-oncology. When I moved to Sweden to train as a surgeon, I didn’t have a specific moment of inspiration. However, a fantastic senior colleague, Leif, approached me early in my training and encouraged me to pursue research. He must have seen something in me—perhaps my curiosity and perseverance. Since then, what drives me to continue with research is the patients and seeing them in situations where I cannot say why I am doing what I am doing. I need to have evidence for the treatments I recommend. For instance, if I tell a patient they need a mastectomy or need lymph nodes removed and I know it’s going to hurt the patient in the long term, I want to be certain that these actions are necessary. It’s basically this unhappy feeling in the clinic when I just don’t know if I’m doing the right thing.

About how I became a surgeon, I was going to become a psychosomatic doctor, which is a combination of internal medicine, psychology, and psychotherapy in Berlin. Everything was planned: exams, research, post-doc work. Then, during my final obligatory year as a student, I decided to go to Sweden for my mandatory surgical training, as my best friend Michaela had moved there. During these three or four months at the clinic, I just felt like I was home. The hands-on nature of surgery, the immediate impact of the work, and the ability to cure people felt incredibly appealing. My mother, a psychologist, once told me, ‘ You can’t cure people with a knife’ but I believe you can!

RS: That’s interesting. I thought you always wanted to be a surgeon. I’ve seen you perform surgery, and it was incredibly inspiring. What was the first research question you began to investigate in your career, and how has this changed over time?

JdB: My initial research in Berlin was completely different—it was a study on the treatment of hepatitis C. However, I set that aside. My first projects involved sentinel lymph node biopsy in breast cancer, around the early 2000s. At that time, we still performed many axillary clearances, so we started implementing sentinel lymph node biopsies while still performing clearances to ensure the sentinel nodes weren’t giving false-negative results. That’s how I started. We were still discussing the performance and accuracy of sentinel lymph node biopsy in large tumors as a different project. I also delved into tumor immunology, studying the immune response in lymph nodes, which was quite exciting.

RS: So, you’ve been doing axillary surgery research for almost 25 years now. That’s quite impressive. I guess you know the subject very well after all this time.

JdB: Yes, I’ve been deeply involved in it for many years.

RS: Let’s talk about our recent publication, which is a very hot topic right now. The results have been presented worldwide. The research conducted aims to reduce the burden of surgery in breast cancer patients, such as the invasiveness of surgery and potential complications. And now this research has resulted in the first surgical paper to have been published in NEJM in the last 17 years. The SENOMAC trial started back in 2015; how did you come up with the idea to start this study?

JdB: It was of course not just my idea, but we were a team from the beginning. When we started the study, we thought it would just confirm the results of two previous studies conducted in the US and the Netherlands, which had shown similar outcomes. But, those studies faced a lot of criticism, especially the US one, due to their small sample sizes and lack of statistical power. They weren’t convincing enough. Back then, I argued against continuing axillary dissections but the Swedish National Guidelines committee deemed the data too weak to support stopping them. So, we kept performing axillary dissections, which felt frustrating. We were then a small group of people who decided we’d try to start a trial adding more data to the area and broadening the population characteristics to also embrace new questions. For me, this research was a tool to reduce the number of dissections because if guidelines don’t allow you to do less, you need to research to prove the point, and at the same time, research allows patients to get access to a more modern approach.

Before our study was complete, the Swedish guidelines changed, allowing the omission of axillary dissections for certain patients, and replacing it with radiotherapy. So, I didn’t expect our study to have such a significant impact. While it doesn’t change the Swedish guidelines, it significantly influenced guidelines in other countries. I’ve received many inquiries about the inclusion criteria, such as whether we included patients with extra-nodal extension and if our findings apply to various patient types. And then I noticed that people have probably not just used these older trial data in their full scale, but they’ve used it for some subgroups. They still hesitate to omit axillary dissection in certain types of patients. And I think our study, because it’s so large and has full statistical power, is the first to provide definitive answers to these questions. That’s fantastic and it’s gratifying to see its impact.

RS: Where is this already implemented? What kind of impact will this research have?

JdB: Before the study, I believed that many countries already omitted axillary dissection. The big question was regarding mastectomy patients, those undergoing removal of the entire breast, because we lacked data on that group. The impact of our study is significant here.

Some large countries had already implemented omitting axillary clearance, but they didn’t apply it to patients where metastasis had grown outside the lymph node. After our study, they began to include these patients as well, seeing similar outcomes. Many guidelines adapted these older trials with specific limitations, and I think our study helped to remove those limitations. While I don’t know every single country’s guidelines, I believe many were already moving towards reducing axillary dissection for more subgroups.

In the US, the first trials on omitting axillary clearance in breast-conserving surgery were conducted around 2011-2012. They allowed the omission of axillary clearance in these cases but not in mastectomy patients. It appears their guidelines suggest considering it, but it wasn’t standard practice. Our study is now providing the necessary evidence to support broader implementation, including for mastectomy patients.

RS: How would you describe the results of your research to a non-clinician, such as a patient? What kind of impact could this research have on them?

JdB: For a patient, I would explain it like this: Normally, if we don’t see any signs that cancer has spread to the armpit, we remove something called a sentinel lymph node. This is the first lymph node in the armpit which receives lymphatic fluid from the breast, and sometimes, cancer cells if the tumor spreads. We typically remove one, two, or three of these nodes. And then, if we find signs of metastasis in these nodes, previously, we used to perform an additional operation to remove more lymph nodes from the armpit—usually more than 10. This often led to problems with the arm, such as swelling, pain, and restricted movement in the arm, which could last a long time.

Now, our research shows that removing more lymph nodes does not improve survival rates or reduce the risk of cancer recurrence. So, we can safely leave the remaining lymph nodes in place even if the disease may be present in these nodes. This makes a big difference in the patient’s recovery and quality of life.

RS: You participate actively in real-world evidence studies, observational studies, and clinical trials. Why do you think that is important? Can they complement each other, and what’s your view on that?

JdB: In my field, many questions will never be addressed in randomized trials. For example, deciding whether to perform a mastectomy or a lumpectomy, or whether or not to have an immediate breast reconstruction, can’t be left to a coin toss today. Patients need to be involved in the decision-making process, weighing the pros and cons to make informed choices.

For issues like breast conservation versus mastectomy, new randomized trials beyond those performed in the 70s and 80s are unlikely. Instead, we rely on high-quality, population-based databases with comprehensive and reliable data. These databases allow us to adjust for all these confounders that we know we have and provide insights that randomized trials cannot. There are many similar questions where patient choice is paramount, such as whether to undergo breast reconstruction.

Prospective cohort studies also play a crucial role. If there’s evidence suggesting a new method might be better, but the existing trials are not conclusive enough, we can implement this method in a prospective cohort study. This allows us to monitor patients closely and ensure they receive modern treatment while still being able to assess the method’s effectiveness. If the method turns out to be suboptimal, we can identify this and adjust accordingly.

RS: As I mentioned earlier, this project was completed in cooperation with Cytel’s Real-World Evidence team. Why did you choose to partner with us on this project?

JdB: We specifically chose to work with you because our colleague, Anna Johansson, and I discussed the idea of breast conservation versus mastectomy. When we needed someone for the hands-on statistical analysis, Anna recommended you because of the previous positive experience and partnership we have had.

Before we started our collaboration, I mostly did statistics myself. However, with increasing study sizes and the need for randomized trials, I felt the quality had to be completely watertight, and you provided that. I also quite like our collaborative discussions and your proactive ideas. You’re not just executing tasks; we have a dialogue and we brainstorm our options and come up with solutions to the problems that arise throughout the process. Our team, including Anna, shares a commitment to timelines and schedules, making us very effective. When we wrote the articles, we successfully streamlined the process and ensured everyone contributed and helped.

RB: I agree. Our close collaboration and communication work very well. You explain the clinical aspects clearly, and I can, most of the time, explain the statistical context of everything. Even tasks like data cleaning and checks, which could be tedious, were smooth due to our excellent communication. It’s wonderful to have a good working relationship with sponsors, and it’s been a great experience working with you and Anna.

Interested in learning more about Cytel’s Real-World Evidence solutions? Read more.

Note:

[1] Omitting Axillary Dissection in Breast Cancer with Sentinel-Node Metastases | New England Journal of Medicine (nejm.org)

Using Quantitative Bias Analysis in Real World Data Strategy

The gold standard for assessing the efficacy for a medicine continues to be RCTs, however, for many reasons (disease rarity and/or ethical concerns), two-arm trials with adequate power may be infeasible. In such cases, single-arm trials or a purely observational study are conducted. To evaluate comparative treatment effects using data from single-arm trials, non-randomized studies, or indirect treatment comparisons (ITCs), are designed to incorporate external data sources. These include datasets from other historical trials or observational, real-world data (RWD).

What it means to be a lead analyst on a Global COVID-19 Trial

The TOGETHER Trial for COVID-19 therapies, designed by clinical trial specialists at Cytel won the Society for Clinical Trials David Sackett Trial of the Year Award for 2021. I interviewed Hinda Ruton, Research Associate at Cytel, who made significant contributions as Lead Analyst to several studies in TOGETHER Trial.

Hinda has been working as a lead statistician and statistical programmer for clinical trials, and statistician for real world data (RWD) analysis. Prior to joining Cytel, he was the program coordinator of the Rwanda Human Resources for Health program in the Ministry of Health. He coordinated a program of $150 million USD, aiming to build the capacity of health professionals. This was done in collaboration with 25 of the best teaching institutions in the US. Hinda has an academic appointment at the University of Rwanda where he teaches biostatistics and information management systems.

Read more »

Writing a Successful Study Protocol for Real-World Evidence Studies

Real-world evidence studies are becoming increasingly popular in pharmaceutical development. But to ensure such studies are feasible and of high scientific quality, a well-written study protocol is essential. Let’s take a closer look at how to write a successful study protocol for real-world evidence studies: Read more »

Newer Posts

Older Posts

Discovery

Phase I-III Clinical Trials

Commercialization

Real-World Evidence Solutions

Clinical Trial Design

Trial Delivery

Advanced Analytics

Specialty Areas

Strategic Consulting

Beyond Functional Service Provider

Project-Based Analytical Solutions

Trial Design Software

Trial Implementation and Decision Support Software

LiveSLR® Software for Systematic Literature Reviews

Our Solutions

Quick Links

Quick Links

Quick Links

Quick Links

Quick Links

Quick Links

Quick Links

Quick Links

Quick Links

Quick Links

Quick Links

Quick Links

Quick Links

Quick Links

About Us

Quick Links

Insights

Quick Links

Careers

Quick Links

What are CAR T-cell therapies?

CAR T-cell therapies: Highlighting equity concerns in access to innovative treatments

Exploring equity factors in HTAs of CAR T-cell therapies and the journey toward inclusive access

Addressing equity concerns in CAR T-cell therapies: Strategies for inclusive access

1. Define clear objectives: Focus on what matters most

2. Streamline safety data collection

3. Optimize operational feasibility

4. Leverage real-world evidence (RWE)

5. Harness technology for smarter data collection

6. Engage regulators early

7. Analyze and learn from past trials

Final takeaways

Here’s a glimpse of what we might see in 2025

One of the most exciting areas of innovation is the emergence of agentic AI and the use of digital twins and synthetic data.

The convergence of these trends will require a collaboration of clinical data scientists

The Bayesian borrowing concept

Figure 1. Bayesian borrowing

Figure 2. Example of QBA for Bayesian borrowing

FDA and HTA submissions incorporated with Bayesian borrowing methods

The future of Bayesian borrowing

Notes