One2Treat was born out of my frustration as a biostatistician with conventional approaches to clinical trial research. Over the years, I’ve witnessed the limitations of relying solely on a single primary criterion for evaluating new treatments. This narrow focus often overlooks valuable data and potential therapeutic benefits.

In many clinical trials, vast amounts of patient data are collected, yet the analysis tends to focus narrowly on one principal endpoint, with just a few secondary endpoints considered. This means that a significant portion of collected data does not influence regulatory decisions. Treatments that might have shown promise under a more comprehensive evaluation end up being disregarded.

One memorable example is the addition of oxaliplatin to 5-fluorouracil for metastatic colorectal cancer. Initially, the treatment failed to gain FDA approval when its primary endpoint, Overall Survival (OS), missed statistical significance. However, it exhibited significant benefits in progression-free survival. This discrepancy underscores the complexity of evaluating treatments solely on OS, especially when subsequent lines of therapy can influence survival outcomes. Oxaliplatin was ultimately approved a few years later and remains a standard of care today, reflecting its proven efficacy. Still, I often wondered how many patients could have benefited from this treatment within the few extra years it took to get approved.

Another case is the addition of erlotinib to gemcitabine for metastatic pancreatic cancer. This combination was approved based on reaching its primary endpoint, which demonstrated a mild yet statistically significant improvement in progression-free survival. However, this evaluation did not consider important factors such as toxicities and a decrease in quality of life, failing to capture the drug’s overall benefit-risk profile. Although erlotinib was approved, its use in this indication was extremely limited.

The need for a more inclusive and patient-focused trial design became even clearer when one of my relatives shared his challenging experiences participating in a clinical trial. This story sheds light on a widespread issue: participants frequently receive scant details about the trial’s purpose, the drug being tested, or the significance of their involvement, leaving them feeling overlooked. Such a mismatch between trial procedures and patient engagement highlights the urgent need for change. It emphasizes the necessity of a paradigm shift that would place patient insights and experiences at the heart of clinical research.

To bridge these gaps, I’ve dedicated myself to developing the Generalized Pairwise Comparisons (GPC) statistical method. This innovative approach enables the comprehensive analysis of treatment effects across multiple clinically relevant criteria, offering a fuller picture of a treatment’s potential benefits. One2Treat was founded to broaden the use of this methodology through the creation of software focused on comprehensive benefit-risk analysis. Our aim is to democratize access to advanced statistical analysis, making it accessible to a wide range of users irrespective of their programming or statistical expertise. By emphasizing simplicity and efficiency, One2Treat aims to make advanced statistical analysis a standard part of clinical research, empowering more informed treatment decisions.

One2Treat is dedicated to transforming clinical research by making trials more patient-centric and data-informed. By leveraging our software to consider a broader range of patient-relevant outcomes, we are not only improving the precision of clinical studies but also accelerating the path to market for promising treatments. As we move forward, One2Treat is committed to enhancing drug development processes, ensuring that every piece of patient data is valued, and every potential treatment is thoroughly assessed, incorporating all key patient’s needs. Join us in this mission to revolutionize clinical trials and improve patient care.

This blog was originally published by the Association of Clinical Research Professionals (ACRP).

Rare disease clinical trials face significant challenges. A study analyzing 199 discontinued rare disease trials found that insufficient patient accrual was the primary cause,¹ with up to 33% of trials failing due to this cause. With small patient populations, leveraging innovative statistical methodologies that can enhance statistical power is crucial. It enables the detection of treatment effects with a reduced sample size, making the study more feasible. Traditional clinical trials typically rely on a single primary endpoint, such as overall survival or disease progression. However, in rare diseases, multi-dimensional outcomes—including efficacy, safety, and quality of life—are often equally important. 

The Net Treatment Benefit (NTB), estimated from the Generalized Pairwise Comparisons (GPC) methodology, provides a promising solution by integrating multiple clinical outcomes into a single assessment, extracting more information from the data.² This innovative approach allows for more powerful, patient-focused analyses, thus enabling smaller, faster, and more cost-effective trials without sacrificing scientific rigor. 

The NTB is the difference between the probability that a random patient in the treatment group has a more favorable outcome than a random patient in the control group and the probability of the opposite occurring. By considering multiple endpoints within a single analysis, NTB provides a more comprehensive picture of a treatment’s overall benefit-risk profile. Unlike traditional methods, NTB enables prioritization of outcomes based on clinical importance and patient preferences, offering a more patient-focused approach to clinical trial analysis. 

Reducing Sample Size in Rare Disease Trials 

Since most clinical trials estimate treatment effects using a single outcome, they often require a large sample size to achieve statistical significance and demonstrate the treatment’s superiority. The NTB can improve statistical power by borrowing information from multiple outcomes, ranking them hierarchically, and allowing clinically meaningful thresholds to distinguish relevant differences. 

This is especially beneficial for rare diseases, where patient recruitment is challenging, and every patient is invaluable. A reduced sample size means trials can be conducted more quickly and efficiently, which minimizes costs and allows promising new therapies to reach patients sooner. In the context of rare diseases, where treatment options are often limited, any method that speeds up research while maintaining robust statistical evaluation is a major advancement. 

Specifically in the rare disease domain, a post-hoc analysis of the randomized, double-blind, Phase III COMET trial, prioritizing the primary (forced vital capacity) and secondary outcome (6MWT), provided evidence of efficacy of avalglucosidase alfa therapy (n = 51) over alglucosidase alfa (n = 49) in Pompe disease, while the original analysis failed to significantly show superiority of treatment on the primary endpoint.³

While no drug has been approved by regulatory bodies using the NTB for rare diseases, the methodology is widely used in other therapeutic areas and has been successfully included in regulatory submissions. Notably, it was used as the primary analysis in the Phase III ATTR-ACT trial, where the analysis prioritized time to death over time to hospitalization. Results demonstrated the efficacy of tafamidis (n = 264) over placebo (n = 177) and led to the drug approval for patients with transthyretin amyloid cardiomyopathy.⁴

Conclusion 

The Net Treatment Benefit represents a transformative step in clinical trial design, particularly for rare diseases where smaller sample sizes are more common due to the limited patient populations. By incorporating multiple outcomes into a single, more comprehensive estimation, the NTB enables more efficient, transparent, and patient-focused trials. Unlike traditional methodologies, which often rely on a single endpoint for regulatory approval, NTB provides a more holistic assessment of treatment effects. 

With growing regulatory support and clear advantages in reducing sample size, NTB has the potential to reshape the landscape of rare disease clinical trials, making them more feasible, cost-effective, and aligned with patient needs. As clinical research continues to evolve, adopting NTB-based approaches will be crucial in accelerating the development of new treatments and improving outcomes for patients with rare diseases. 

References 

1. Rees CA, Pica N, Monuteaux MC, Bourgeois FT. 2019. Noncompletion and nonpublication of trials studying rare diseases: a cross-sectional analysis. PLoS Medicine 16(11):e1002966.
2. Buyse M, Verbeeck J, Saad ED, Backer MD, Deltuvaite-Thomas V, Molenberghs G (Eds.). 2025. Handbook of Generalized Pairwise Comparisons: Methods for Patient-Centric Analysis (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781003390855
3. Verbeeck J, Dirani M, Bauer JW, Hilgers RD, Molenberghs G, Nabbout R. 2023. Composite endpoints, including patient reported outcomes, in rare diseases. Orphanet Journal of Rare Diseases 18(1) :262.
4. Maurer MS, Schwartz JH, Gundapaneni B, Elliott PM, Merlini G, Waddington-Cruz M, … and Rapezzi C. 2018. Tafamidis treatment for patients with transthyretin amyloid cardiomyopathy. New England Journal of Medicine 379(11):1007–16.

Contributed by Tom Mann, Clinical Solutions Engagement Lead at One2Treat. Mann has more than 15 years of experience in technology start-ups and scale-ups in settings where he played a pivotal role in driving customer engagement, marketing initiatives, and strategic partnerships. At One2Treat, he helps develop solutions that integrate key patient-relevant outcomes into a single holistic treatment assessment, ensuring that the company’s approach remains both innovative and patient-focused. 

Original interview published in Outsourcing Pharma.

Could you give us an overview of your work? 

I have recently been promoted to Head of Solution Value at One2Treat, in this new role, I’m ensuring the development of our software solution and strategic services are fully aligned with our customer needs and company purpose.

The personal development and growth of each of the team members is also an important part of my role.

At One2Treat we develop methods and advanced software that enable a holistic evaluation of treatment effects by integrating all key patient-relevant outcomes into a single comprehensive assessment of the Net Treatment Benefit.

When did you realize you were interested in science- as a young child, teen or older? 

From a young age, I always had a thirst for understanding. For example, whenever a topic was discussed at school, I felt curious and would do my own research to learn more.

When growing up, I became more and more interested in math and chemistry.

I initially considered being a medical doctor or a pharmacist, as patient care was central to my aspirations. Ultimately, I decided to pursue mathematics, as it combined my analytical mindset with my interest in problem-solving. After earning my degree in mathematics, I further specialized with a master’s in statistics, where I discovered a passion for applying data to real-world challenges. This naturally led me to focus on biostatistics, a field that allowed me to merge my love for science with my goal of contributing to healthcare through data-driven insights.

Could you describe your personal journey bringing us to where you are now? 

At the end of high school, I joined university studying mathematics, with no clear idea at that moment about where it would lead me. I had classes with an inspiring professor who regularly used practical examples from his experience working in clinical trials at a pharma company.  

My future suddenly became clear… I wanted to work in clinical trials to fulfill my dreams as a child: “improving patients well-being by actively contributing to the development of better treatments”.

After graduating, I applied to a large pharma company (Bristol-Myers Squibb) where I worked as a biostatistician for 9 years. I continued my journey by moving to a CRO (IDDI), where I collaborated with different pharma companies, thus increasing my knowledge about different therapeutic areas and diseases. Within IDDI I had the opportunity to grow and transition to a Project Management role, allowing me to have a broader picture of drug development; not only statistics, but all the different steps (protocol and study design, randomization, data management, statistical analyses until full package submission).

In 2023, I took the opportunity to join One2Treat, an innovative technology start-up, focusing on software solutions that are driving advancements in patient-focused drug development.

My reasons for joining One2Treat were twofold. First, I was convinced by the methodology leveraging multiple dimensions in evaluating the treatment effect in randomized clinical trials. Secondly, I could be a key contributor in a young company with a blank page and an exciting focus.

What challenges did you face- as a woman or otherwise- along the way and what is the most valuable lesson you have learned? 

As a teenager, I was told by a teacher that I would never succeed in sciences. After failing a test, I sought clarification and was instead encouraged to change my goal. Perhaps it was because I was a girl—who knows? While this moment shook my self-confidence for years, it also became a turning point.

The most valuable lessons I’ve learned since then are to believe in yourself and your dreams, no matter the obstacles. If you trust in your abilities, you can achieve incredible things. And most importantly, never lose sight of your focus—it’s what guides you to success.

What ignites your passion in your current role? 

I am motivated by the tangible progress we’re making at One2Treat toward fully engaging patients in the drug development process. It’s inspiring to see the shift in the industry, where patients are becoming central to clinical trials. It is exciting that at One2Treat, we drive innovation by ensuring that clinical trials are increasingly patient-focused. Knowing that our efforts contribute to making treatments more accessible, relevant, and impactful for patients keeps me deeply committed to this work.

On a more personal level, I also find great satisfaction in tackling the challenge of balancing complex elements such as advanced software, effective people development, and high-quality services that bring value to our customers. Successfully integrating these components requires both strategic thinking and empathy. This dynamic aspect of my role constantly pushes me to grow and evolve, both professionally and personally.

What is your current work ethos/style? 

My current work ethos revolves around collaboration, adaptability, empathy, and strong analytical skills. I believe that fostering open communication and mutual respect within a team is key to aligning diverse perspectives and skills toward a shared goal, ensuring that everyone feels valued and heard. My background in mathematics and statistics equips me to approach challenges with a structured, problem-solving mindset, allowing me to find innovative and data-driven solutions.

Ultimately, my work style is defined by a balance of strategic focus, creative problem-solving, and a deep commitment to making a positive difference in everything I do.

Could you share some advice for young women starting to develop an interest in science or wanting to pursue a career like yours?

My advice for young women interested in science or pursuing a similar career is to embrace your curiosity and never be afraid to ask questions.

Believe in your abilities and trust that your perspective is valuable, even if you face doubts or challenges along the way… and you can be sure that you will! Science thrives on diverse viewpoints, and your own personal contributions will help shape the field in meaningful ways.

Authors: Tom Mann & Samuel Salvaggio

Content published in Outsourcing Pharma.

1. What is driving the current evolution in clinical trial design?

There is an increasing focus on including patient perspectives in clinical trial design. Regulatory bodies are drafting new guidance that ultimately will drive pharmaceutical companies to truly integrate patient voices into trial design. This is crucial not only for ensuring real-world relevance, but it will also expedite the path to market. Some innovative companies are already embedding patient perspectives early in the process to deliver treatments that will align with patient needs and preferences. This shift in the clinical trial landscape will enhance both the therapeutic impact and the perceived value of new treatments.

Recent advancements in statistical methodologies and computing power are another key driver, enabling better utilization of the vast data collected during clinical trials. Recent regulatory initiatives, such as the FDA’s Project Optimus, are also shaping this evolution. Indeed, Project Optimus is trying to refocus oncology clinical trials to switch from the paradigm of maximum tolerated dose to introduce dose optimization, encouraging sponsors to ensure that treatments are effective but also better tolerated by patients.

As this trend continues, regulatory agencies are likely to expand their guidance to accommodate the new trend of assessing treatment effects through hierarchical composite endpoints that are multi-dimensional and ensure incorporating input from patients, advocacy groups, clinicians, and sponsors. By fostering clinical trials that assess treatment effect more holistically, the industry can improve treatment adoption and efficacy.

2. How does evaluating treatment effects from multiple assessments improve decision-making in clinical research?

Clinical trials generate vast amounts of data, yet regulatory approval relies on the assessment of the effect of a single primary outcome. While this can simplify analysis, it also underutilizes valuable information. Innovative and robust statistical methodologies allow for better use of all the additional data created during clinical trials and we can leverage this data to better estimate treatment effects. One of those methodologies leverages multiple outcomes hierarchically to estimate the Net Treatment Benefit (NTB) to reflect treatment effects.

With numerous papers published in peer-reviewed journals these past twenty years, Generalized Pairwise Comparisons, the methodology estimating the Net Treatment Benefit, established itself as a robust method to estimate treatment effects. Unlike traditional statistical assessments, it can integrate multiple clinically relevant outcomes- such as safety, efficacy and quality of life- hierarchically into a single statistical analysis. This approach provides a holistic view of a treatment’s value and resonates with diverse stakeholders, from statisticians to physicians.

In the end, the ultimate vision would be to allow patients directly to give input on how to build an endpoint that prioritizes clinical outcomes reflecting their preferences. Indeed, the stakeholders involved in clinical research might have different visions on what would be meaningful as an endpoint. For example, for some patients minimizing severe side effects may be as important as achieving clinical efficacy. Evaluating multiple outcomes acknowledges these varying perspectives, allowing for more nuanced and patient-relevant decision-making in clinical research.

3. How can we integrate patient perspectives into phase III clinical trials? What are the benefits and challenges of this approach?

Incorporating patient perspectives into clinical trials helps to ensure that these studies reflect the lived experiences of those they are designed to serve. This alignment not only enhances the perceived value of treatments but also fosters better patient engagement. By involving patients in selecting trial endpoints, researchers can produce data that is more relevant to real-world applications, ultimately leading to faster adoption and higher perceived value for the treatment.

Emerging technologies offer promising solutions. For instance, digital tools can capture patient preferences effectively and balance them against sponsor and clinician guidance to design endpoints based on patient real world needs.

However, achieving this integration is not without challenges as clinical research can be a conservative sector. Clinical and statistical innovation needs comprehensive evidence and case studies to be traditionally considered for trial design, making it slow to fully adopt these patient-centric approaches. Overcoming these hurdles requires a shift in perspective and the development of more effective strategies and tools to balance the needs of all stakeholders. One solution would be to discuss as soon as possible with regulators to propose these innovations and hear their feedback.

4. How does early strategic planning in trial design influence the efficiency and success of market access?

Strategic planning during early trial phases is pivotal for ensuring both efficient execution and successful market access. Engaging patients, payers, and clinicians upfront allows companies to identify key dimensions of treatment efficacy, safety, and tolerability. This collaborative approach ensures that trial data is collected with the aim to address the priorities of all stakeholders.

From a statistical perspective when using these innovative methods, leveraging more of the data collected during a trial will increase the statistical power and, thus reduces the number of patients needed for a trial. This in turn reduces time to market and costs, a win-win for patients and sponsors.

In terms of market access, the tendency for research and development departments to focus mainly on a treatment’s efficacy can be detrimental. Indeed, payers demand more comprehensive evidence, including real-world applicability and quantitative benefit-risk assessments. Estimators like the Net Treatment Benefit, which can integrate patient preferences and estimate multidimensional endpoints, enable a comprehensive benefit-risk assessment of a potential new treatment, making the data more compelling for market stakeholders.

Early planning is crucial to align trial outcomes with the complex needs of patients, clinicians, sponsors, regulators and payers. This will ensure smoother approvals and faster commercialization.

5. How have specific therapeutic areas benefited from the multidimensional assessment of treatment effects? Why are some areas particularly suitable for this approach?

Multidimensional assessment has proven particularly beneficial in therapeutic areas like cardiovascular disease and rare diseases. In cardiovascular research, numerous metrics, such as heart attack incidence and quality-of-life scores, are available, yet clinical events are often sparse. By prioritizing and hierarchizing these metrics, multidimensional assessments preserve their medical relevance while maximizing statistical power.

Rare diseases also stand to gain significantly. These conditions often involve small patient populations and complex disease profiles, making traditional trial designs challenging. Integrating multiple outcomes into a single evaluation framework can reduce the required sample size and provide a more comprehensive picture of treatment efficacy. This approach is invaluable in rare diseases, where patient recruitment is inherently limited.

6. Can you describe how this fits into the current regulatory landscape? What are the benefits and challenges?

Regulatory agencies, such as the FDA, are increasingly emphasizing holistic approaches to treatment evaluation. Recent guidance in rare diseases, for example, encourages the inclusion of patient perspectives and multidimensional assessments. While methodologies like Generalized Pairwise Comparisons and Net Treatment Benefit have gained traction in areas like cardiovascular research, other fields, such as oncology, are still awaiting their first trials employing Net Treatment Benefit as a primary endpoint.

Initiatives like Project Optimus demonstrate the alignment between regulatory goals and multidimensional methodologies. Dose optimization, a central tenet of the project, balances efficacy and safety—key elements in any benefit-risk assessment.

One of the challenges is promoting the adoption of these methodologies across therapeutic areas. While multidimensional approaches are conceptually straightforward, their statistical underpinnings can be complex, posing interpretability challenges.

Addressing these hurdles requires robust education and collaboration among biostatisticians, clinicians, and regulators to ensure broader acceptance and implementation.

Regulatory guidance will push the industry further, but the true transformation will come from companies that embed patient insights early in trial development. Tools that capture patient input in clinical research as early as before Phase II trials will stand out as game-changers. It will shape trials that resonate with patient needs, define new meaningful endpoints, and accelerate both approvals and time to market. This shift will establish patient-centricity as the gold standard for clinical research. 

How the inclusion of measures centered on net treatment benefit can drive an effective multifaceted approach.

Patient-centricity has become a guiding principle for clinical research and treatment evaluation. There is a strong movement in clinical research to actively involve patients at the center of decision-making, with their preferences, quality of life, and individual values increasingly recognized as key elements in assessing treatment success. The net treatment benefit (NTB) embodies this patient-centered approach by allowing integration of diverse clinical outcomes into a single comprehensive metric. By prioritizing outcomes based on patient input, NTB offers a more personalized and transparent way to evaluate treatment effects, empowering patients to play an active role in their healthcare journey.

The robust methodology behind NTB

NTB is a measure of treatment effects estimated from generalized pairwise comparisons (GPC), a statistical method that stems from the well-known Mann-Whitney Wilcoxon non-parametric test.¹ The primary innovation of GPC is to provide a global assessment of treatment effects through the hierarchical integration of any type of outcomes, such as efficacy, safety, and quality of life. By allowing the integration of multiple outcomes within a single analysis, this approach can overcome the limitations of traditional methods that focus on a single primary endpoint. Indeed, analyzing outcomes hierarchically rather than marginally better reflects their interactions and dependencies.

An advantage of GPC is that its logic is quite simple. The approach sequentially compares each clinical outcome based on a predefined list of priorities. For instance, in a cancer trial, overall survival may be the highest-ranked outcome, followed by cancer progression outcomes, treatment-related adverse events, and quality of life measures. It also allows the use of thresholds of clinical relevance that define a minimum clinical difference required for classifying two pairs. Once the priorities and thresholds are specified, each patient from the experimental arm is compared with all possible patients from the control arm, starting with the highest-priority outcome. The possible outcomes of these comparisons are classified as a “favorable” (if the patient in the experimental group fares better), “unfavorable” (if the control patient does better), or “neutral” (if there is no significant difference between the two patients).

If a pair is classified as neutral, the comparison moves to the next outcome, which might be toxicity or quality of life. Each pair initially classified as “neutral” is then compared on the next outcome and reclassified as “favorable,” “unfavorable,” or “neutral.” This continues until all hierarchical outcomes have been considered. The NTB can be calculated as the difference between the probability of being favorable to the experimental treatment and the probability of being favorable to control. This creates an easily interpretable absolute metric where a NTB value greater than zero indicates that the experimental treatment provides a net benefit over the control treatment, while a value less than zero indicates net harm.

Possible applications and case studies

This emerging class of statistical analysis is sometimes referred to as hierarchized composite outcome and is gaining increased traction in clinical research. One of the most recognized is an alternative estimator called the win ratio (WR). However, as a relative measure, the WR has several limitations. First, it lacks transparency in illustrating how individual outcomes contribute to the overall treatment effect, making it difficult to understand the specific factors driving the observed benefit or harm. Moreover, the WR disregards neutral pairs by assuming they behave similarly to classified pairs, which can be misleading if this assumption does not hold. Recently, two treatments have been approved by FDA’s cardiovascular division using GPC and the WR, the most well-known being tafamidis.²

In contrast, the NTB provides a clearer understanding of treatment effects by explicitly detailing each outcome’s contribution. This makes NTB a more transparent and patient-centered approach, ensuring that each relevant clinical outcome is considered and that the overall estimate reflects a comprehensive picture of patient impact. Recent case studies have demonstrated the benefits of NTB in highlighting differences across a wide range of patient-relevant outcomes, ultimately leading to more informed and patient-aligned decision-making in clinical trials.

The NTB can also address some of the shortcomings associated with non-inferiority trials. As discussed in a recent published paper in Lancet Oncology, non-inferiority trials often face criticism for relegating outcomes such as toxicity and cost to secondary considerations, and for setting very narrow statistical margins that can make it difficult for alternative, potentially patient-friendlier treatments to demonstrate equivalence. These trials frequently require large sample sizes and expensive resources to prove that an alternative is “not worse” than the standard, even when the clinical differences are minimal.³ The NTB, on the other hand, can transform this approach by incorporating multiple outcomes into a superiority benefit-risk analysis, offering a more nuanced and holistic assessment. By prioritizing and integrating different endpoints, NTB helps redefine the evaluation from simply establishing non-inferiority to understanding the full spectrum of benefits and risks, thereby supporting more patient-centered, cost-effective, and meaningful treatment decisions.

Incorporating patient preferences into NTB

One of the most powerful aspects of NTB is its ability to accommodate patient preferences directly into the clinical trial design and analysis. In the traditional trial framework, only outcomes such as overall survival or disease-free survival are used for market approval, while others—such as quality of life, symptom burden, or adverse effects—are secondary considerations. This approach can fail to reflect what truly matters to patients, particularly when dealing with chronic conditions where long-term side effects or daily functioning may outweigh the benefit of the former outcomes. By focusing solely on clinical endpoints, traditional methods may overlook the nuanced needs and desires of patients, limiting the comprehensiveness of the treatment effect evaluation.

The NTB, however, can be estimated based on the outcomes that are most important to individual patients. This patient-centric approach empowers individuals by giving them a voice in the evaluation process. It enables patients to express their preferences and offers a more personalized view of treatment effects. For example, patients may prioritize quality of life and minimal side effects over overall survival, depending on their unique circumstances. By allowing for such individual differences, NTB provides a more holistic understanding of treatment benefits that align closely with patients’ lived experiences.

To facilitate the incorporation of patient preferences, software tools designed to elicit these preferences are essential. These tools can help quantify and integrate patient values, allowing researchers to develop personalized NTB metrics. Such tools exemplify how patient input can be incorporated into the NTB methodology, transforming the traditionally rigid framework of clinical trials into one that is more adaptable to individual patient needs. The FDA currently encourages medical researchers to engage with patients as early as the design phase of clinical trials,4 underscoring the importance of understanding patient priorities from the outset.

Furthermore, relevant methods are needed to elicit patient preferences in the context of treatment effect measures. Current tools and techniques, such as surveys, discrete choice experiments, or conjoint analysis, offer promising approaches but still lack standardization across the industry. While the FDA, in past guidance documents, acknowledges that “quantitative patient preference assessment is an active and evolving research area,”⁵ no standard process has been validated yet. This lack of standardized tools poses a challenge, as pharmaceutical companies may be hesitant to invest in patient-focused methodologies without clear regulatory frameworks.

Significant efforts are needed from the industry to address this unmet need and support patient-focused clinical research. This includes developing and validating standardized methods to elicit patient preferences and ensuring these methods are robust enough to be used in regulatory submissions. By taking these steps, the industry can foster a more patient-centered approach to clinical trials, ultimately leading to treatments that better reflect patient needs and enhance their quality of life.

Tom Mann is Clinical Solutions Engagement Lead; Sarah Kosta, PhD, is Clinical Trial Solutions Analyst; and Samuel Salvaggio, PhD, is Senior Trial Design Lead; all with One2Treat

References

1. Buyse, M. Generalized Pairwise Comparisons of Prioritized Outcomes in the Two‐Sample Problem. Stat Med. 2010. 29 (30), 3245-3257. https://pubmed.ncbi.nlm.nih.gov/21170918/

2. Maurer, M.S.; Schwartz, J.H., Gundapaneni, B., et al. Tafamidis Treatment for Patients with Transthyretin Amyloid Cardiomyopathy. N Engl J Med. 2018. 379 (11), 1007-1016. https://www.nejm.org/doi/full/10.1056/NEJMoa1805689

3. Tannock, I. F.; Buyse, M.; De Backer, M.; et al. The Tyranny of Non-Inferiority Trials. The Lancet Oncol. 2024. 25 (10), e520-e525. https://www.thelancet.com/journals/lanonc/article/PIIS1470-2045(24)00218-3/abstract

4. FDA, FDA Patient-Focused Drug Development Guidance Series for Enhancing the Incorporation of the Patient’s Voice in Medical Product Development and Regulatory Decision Making (February 14, 2024). https://www.fda.gov/drugs/development-approval-process-drugs/fda-patient-focused-drug-development-guidance-series-enhancing-incorporation-patients-voice-medical

5. FDA, Patient Preference Information – Voluntary Submission, Review in Premarket Approval Applications, Humanitarian Device Exemption Applications, and De Novo Requests, and Inclusion in Decision Summaries and Device Labeling (August 2016). https://www.fda.gov/regulatory-information/search-fda-guidance-documents/patient-preference-information-voluntary-submission-review-premarket-approval-applications