Bayesian sample size determination in basket trials borrowing information between subsets

Multidisciplinary considerations for implementing Bayesian borrowing in basket trials

Drug development has historically relied on phase I-III clinical trials including participants sharing the same disease. However, drug development has evolved as the discovery of mechanistic drivers of disease demonstrated that the same therapeutic target may provide benefits across different diseases. A basket trial condenses evaluation of one therapy among multiple related diseases into a single trial and presents an opportunity to borrow information across them rather than viewing each in isolation. Borrowing is a statistical tool but requires a foundation of clinical and therapeutic mechanistic justification. We review the Bayesian borrowing approach, including its assumptions, and provide a framework for how this approach can be evaluated for successful use in a basket trial for drug development.

Inflammation across tissues: can shared cell biology help design smarter trials?

Immune-mediated inflammatory diseases (IMIDs) are responsible for substantial global disease burden and associated health-care costs. Traditional models of research and service delivery silo their management within organ-based medical disciplines. Very often patients with disease in one organ have comorbid involvement in another, suggesting shared pathogenic pathways. Moreover, different IMIDs are often treated with the same drugs (including glucocorticoids, immunoregulators and biologics). Unlocking the cellular basis of these diseases remains a major challenge, leading us to ask why, if these diseases have so much in common, they are not investigated in a common manner. A tissue-based, cellular understanding of inflammation might pave the way for cross-disease, cross-discipline basket trials (testing one drug across two or more diseases) to reduce the risk of failure of early-phase drug development in IMIDs. This new approach will enable rapid assessment of the efficacy of new therapeutic agents in cross-disease translational research in humans.

Design and analysis of umbrella trials: Where do we stand?

Background

The efficiencies that master protocol designs can bring to modern drug development have seen their increased utilization in oncology. Growing interest has also resulted in their consideration in non-oncology settings. Umbrella trials are one class of master protocol design that evaluates multiple targeted therapies in a single disease setting. Despite the existence of several reviews of master protocols, the statistical considerations of umbrella trials have received more limited attention.

Methods

We conduct a systematic review of the literature on umbrella trials, examining both the statistical methods that are available for their design and analysis, and also their use in practice. We pay particular attention to considerations for umbrella designs applied outside of oncology.

Findings

We identified 38 umbrella trials. To date, most umbrella trials have been conducted in early phase settings (73.7%, 28/38) and in oncology (92.1%, 35/38). The quality of statistical information available about conducted umbrella trials to date is poor; for example, it was impossible to ascertain how sample size was determined in the majority of trials (55.3%, 21/38). The literature on statistical methods for umbrella trials is currently sparse.

Conclusions

Umbrella trials have potentially great utility to expedite drug development, including outside of oncology. However, to enable lessons to be effectively learned from early use of such designs, there is a need for higher-quality reporting of umbrella trials. Furthermore, if the potential of umbrella trials is to be realized, further methodological research is required.