Written by Boaz N. Adler, MPA, Director, Global Product Engagement, and Valeria Mazzanti, MPH, Associate Director, Customer Success
Rare disease studies come with their own unique challenges, particularly limited patient populations. However, there are a number of adaptive clinical trial design strategies that can be used to mitigate the challenge of hard-to-recruit patient populations and associated trial risks. Here, we delve into four: sample size optimization, sample size re-estimation, phase II/III seamless designs, and Exact Statistical methodologies, and how each of these can help trial sponsors working with limited rare disease patient populations.
What are rare diseases?
In the United States, a disease is considered rare if it affects fewer than 200,000 people (that is, about 60 per 100,000 people). The European Union defines it as those with a prevalence of less than 5 patients in 10,000. There are over 7,000 known rare diseases to date, such as Sickle Cell Disease, Duchenne Muscular Dystrophy, and Sjogren’s Disease. As medical technology develops, specifically in the areas of gene-mapping, more diseases join this list each year. With the identification and mapping of these diseases, there is also increasing interest in gene-editing and personalized medicines to address these diseases and provide treatments and cures. However, unique challenges persist.
What makes the area of rare disease unique as a field of study?
The most significant challenge in the area of rare diseases is the limited number of patients available for study. This can be exacerbated by the fact that those impacted by rare diseases are often misdiagnosed in the first stages of their disease, complicating their treatment paths and adding to their anxiety and suffering. Another significant challenge in drug development for rare disease is the ability to measure and demonstrate treatment effect. Treatments that are not cures often rely on disease activity scales or quality of life measures, which may require a larger number of study subjects. Cures, on the other hand, may require very long-term follow-up to establish the success of the therapy, thus increasing the costs and administration of such studies.
How can limited population numbers be addressed in clinical trial design and execution?
Limited population size can lead to increasing timelines for patient recruitment, sometimes to untenable lengths; or in cases where the patient population is very rare, there may simply not be enough patients for the application of the typical statistical methodologies, or even fully comparable trials. In the first instance, applying designs that seek to minimize the required sample size can help mitigate both overall study duration and study cost. Such designs may include sample size re-estimation or seamless phase II/III studies. For the latter example, where the number of patients is exceedingly rare, applying Exact Statistical methods, designed to account for missing or sparse data, or using single-arm non-comparative designs may be useful. In cases of non-comparative study designs, external comparison methods relying on real-world evidence such as historical controls or external control arms can be used to augment evidence.
How are some of these challenges mitigated or addressed in clinical trial design?
There are a number of ways trial sponsors can use adaptive clinical trial designs and strategies to account for limited or hard-to-recruit patient populations. Below, we delve into four: Sample-Size Optimization; Sample-Size Reassessment; Phase II/III Seamless Designs; and Exact designs.
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- Sample-Size Optimization is commonplace in most modern study designs, in which sponsors seek to limit, to the extent possible, the number of patients exposed to a clinical protocol. In a rare disease study setting, the need to optimize the expected study sample size is paramount.
- Sample-Size Re-Estimation is a method that allows statisticians to commit a to a certain patient enrollment number based on a specific expected treatment effect, while also specifying a potential increase in that sample size in case the observed treatment effect is somewhat lower than the original estimate. This allows study sponsors to set stages of investment in their trial, and gatekeep additional investment based on interim results.
- Phase II/III Seamless Designs involve combining the phases of a clinical trial into a single trial, with the option to modify the study design based on accumulating data. Adaptive seamless designs can help to reduce the number of patients needed to achieve the study’s objectives, as well as increase the efficiency of the trial by eliminating the need for separate trials for each phase. In the case of rare disease studies, “Graduating” the study population from the phase II of your study into phase III, represents a savings in recruitment timeframes and administration costs, and allows the continued monitoring of this population.
- Exact Statistical Methods are more sensitive to the treatment effect and so are used when there are too few observations of the disease for normal approximation methods. These methods are designed to analyze sparse or missing data and are well suited for rare disease studies, which often have limited datasets for analysis.
- Sample-Size Optimization is commonplace in most modern study designs, in which sponsors seek to limit, to the extent possible, the number of patients exposed to a clinical protocol. In a rare disease study setting, the need to optimize the expected study sample size is paramount.
Final Takeaways
The study of treatments for rare diseases has accelerated over the past two decades with new technologies such as CAR-T and CRISPR offering the specter of all-out cures for these often difficult-to-treat patient populations. Applying the most cutting-edge study design and statistical methodologies will ensure robust results and help pave the way for more of these therapies to reach the market in the coming years.
Interested in learning more about strategies to mitigate the challenge of limited patient populations in rare disease studies? In their upcoming webinar, Boaz Adler and Valeria Mazzanti will discuss this topic and share an example of sample-size re-estimation in a Phase III Sjogren's Disease study. Click below to register. Can’t make the time? Register today and watch it on-demand after the event:
About Boaz N. Adler
Boaz Adler is Director of Global Product Engagement at Cytel. He has served as a Solutions Consultant and Analyst for Life Sciences companies and Health-Tech organizations for over a decade. Boaz’s interests are focused on tech and novel services innovations that contribute to more coherent and robust evidence generation across the drug development cycle. At Cytel, Boaz enhances the connection between Cytel’s software development team and its clients and supports clients in clinical trial optimization projects using Cytel’s cutting-edge technology.
About Valeria Mazzanti
Valeria Mazzanti is Associate Director of Customer Success at Cytel. She is an expert in adaptive clinical trial design methodology and software, including cutting-edge and industry standards such as Solara®, East®, and EnForeSys®. Prior to joining Cytel, Valeria worked in several different academic research laboratories, and has extensive teaching experience at prestigious universities. Valeria completed a Master of Public Health degree specialized in Biostatistics at Columbia University in New York and a Bachelor of Science degree in Behavioral Neuroscience at UCLA.
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