There are various designs for basket trials and the company hasn’t released any details for their phase1b trial. As per FDA master protocol’s guidance each subgroup’s safety will have to be evaluated individually if necessary corrective actions etc.
There is also potentials for information borrowing across subgroups, effective statistical analysis, time and cost savings for certain types of basket trial(Bayesian designs). I am sure the CROs/sponsors would choose the best design for the trial.
Point is trying to interpret and make anticipations for every sentence every word from the company can be quite premature not knowing the real reason again as far as we know the phase1b is ongoing, patients are being enrolled in whatever pattern based on a logical explanation which will supports the overall purpose, “less complex yet more effective basket trial”.
The Benefits of Using Basket Studies in Oncology
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.
https://academic.oup.com/biostatistics/article/23/1/120/5831921
Most recently, more sophisticated methods in the framework of Bayesian model averaging (Madigan and Raftery, 1994; Draper, 1995) have been applied to analyzing basket trials. Psioda and others (2019)average over the complete model space, which is constituted by all models for possible configurations of the subgroups that may demonstrate the same or disparate efficacy. In a model that assumes identical treatment effect among specific subgroups, information is pooled across the corresponding subgroups under the assumption of inter-patient exchangeability.
https://www.iconplc.com/insights/blog/2022/06/14/using-bayesian-based-model-assisted-designs-in-early-phase-oncology-trials/
Studies that want to test a drug in patients with different types of cancer may also benefit from deploying basket designs within a master protocol when obtaining preliminary efficacy data. Extracting information across cohorts can also improve the efficiency of the statistical analysis using a Bayesian framework. As oncology therapeutics and early phase design models evolve, sponsors will benefit from working with a partner experienced in innovative adaptive designs for phase 1 and 2 oncology trials.