The Benefits of Using Basket Studies in Oncology
Currently, there are many treatment options for Cancer such as, Immunotherapy, Radiation Therapy, Chemotherapy etc. If we were to investigate all these options for each indication, it can result in requiring several individual clinical trials. One way to address this is by conducting a Basket trial in which a targeted therapy is evaluated for multiple diseases that share common molecular alterations or risk factors that may help predict whether the patients will respond to the given therapy. In Oncology, an increasing number of biomarker-targeted therapies are now being investigated and approved.
Why Use Basket Studies?
Over the years, the use of Master Protocols, such as basket studies, has grown considerably and its use will only increase in the next few years. Basket studies are generally used for practical reasons:
- Where there is a strong interest to explore multiple subpopulations simultaneously in a single study in early phase trials (especially in immuno-oncology or target therapy).
- They allow more efficient use of data which is helpful as the sample sizes are often quite limited.
- Basket studies also factor in ethical considerations as they allow interim decisions to stop for futility if no or very low efficacy signals are observed for a particular subpopulation.
Basket Trial Designs
Basket trials are quite flexible in terms of design; they can be Bayesian, Frequentist, Adaptive or a mix of any of these. Bayesian basket trials are flexible and efficient as a range of design elements can be explored and implemented. However, there is a trade-off between efficiency and complexity. The complexity of basket studies leads to challenges around the study design, statistical modeling and analysis, statistical properties, and operational considerations.
In a recent Cytel webinar on Expanding Applications of Master Protocols, James Matcham, VP Strategic Consulting at Cytel, presents on how the statistical approaches have developed from treating each indication separately, to Bayesian designs where information can be shared among indications to reduce overall sample size, time and costs.
In Cytel’s East Bayes, we implement a module of Basket Trial Designs and use simulation-based power calculation to evaluate four Bayesian approaches, including the Bayesian hierarchical model (BBHM) proposed by Berry et al. (2013), the calibrated Bayesian hierarchical model (CBHM) by Chu and Yuan (2018a), the exchangeabilitynonexchangeability (EXNEX) method in Neuenschwander et al. (2016) and a novel multiple cohort expansion (MUCE) method in Lyu et al. (2020). Users may choose desirable designs based on the software provided in this module. During the webinar, James highlights the similarities and differences between these Bayesian approaches.
Click below to watch the on demand webinar.
About the Author of Blog:
Mansha Sachdev specializes in content creation and knowledge management. She holds an MBA degree and has 11 years of experience in handling various facets of marketing, across industries. At Cytel, Mansha is a Content Marketing Manager and is responsible for producing informative content that is related to the pharmaceutical and medical devices industries.