Basket trials in very rare diseases: are they feasible?

Background: In recent years, innovative designs have been proposed in the context of personalized medicine to study the effect of single/multiple drugs on multiple/single sub-populations simultaneously. Specifically, basket trials are used to study a single targeted therapy in multiple diseases or disease types sharing common genetic characteristics. This approach is very useful in rare diseases, where basket trials allow for a more efficient analysis due to borrowing of information across sub-trials. Explicitly, the treatment effect in each sub-trial may provide information on the treatment effect in other sub-trials. Our aim is to assess the feasibility and robustness of a basket design in the setting of very rare diseases, an even more challenging situation. Methods: The clinical setting of interest involves a few trials, each one using the same therapy on very small numbers of patients with different diseases. We considered a single arm trial with a continuous endpoint because it might be more informative, especially when the innovative treatment is highly effective. We evaluated adaptations of two methods originally proposed for binary outcomes (i.e. Bayesian hierarchical model1 and Exchangeability–Nonexchangeability model2) and one method for a continuous variable but in a two arms context (i.e. Treatment Response Borrowing method3). Our simulation study considered various scenarios characterised by the treatment effects of three subgroups, each involving 7 or 15 subjects, compared with a benchmark of 50 subjects. Results: The results of the simulation study suggest that basket trials are feasible even in the context of very rare diseases, especially when the effect size is high (or very low) and consistent across sub-trials. When the treatment effect is not sufficiently large more attention should be paid to the sample size and the method choice. Conclusions: Operating characteristics of the different approaches showed promising results. This encourages our further work to investigate more complex scenarios. References: 1. Berry SM, Broglio KR, Groshen S, Berry DA. Bayesian hierarchical modeling of patient subpopulations: Efficient designs of Phase II oncology clinical trials. Clinical Trials. 2013;10(5):720-734. doi:10.1177/1740774513497539 2. Neuenschwander B, Wandel S, Roychoudhury S, Bailey S. Robust exchangeability designs for early phase clinical trials with multiple strata. Pharm Stat. 2016 Mar-Apr;15(2):123-34. doi: 10.1002/pst.1730. 3. Ouma, L.O., Grayling, M.J., Wason, J.M.S. & Zheng, H. (2022) Bayesian modelling strategies for borrowing of information in randomised basket trials. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71(5), 2014–2037. Available from: https://doi.org/10.1111/rssc.12602