Speaker
Description
Modern therapeutic agents in cancer therapy often target specific genetic traits of the tumor. Whenever these traits are independent of the tissue in which the tumor is located, the therapeutic agent may be tissue-agnostic, meaning that it can be applied regardless of location. Clinical trials for such tissue-agnostic therapies often have small sample sizes. Hence, it is efficient to recruit patients regardless of their tumor location in a single trial (e.g. NSCLC, colorectal cancer, and multiple myeloma in a single trial). Such a trial is called basket trial as all subcohorts are “collected in a single basket”.
Basket trials come with a statistical challenge concerning analysis. On the one hand, a completely separate analysis of the different cohorts is guaranteed to be unbiased at the price of low power due to the small sample size. On the other hand, a pooled analysis will have higher power at the price of potential bias in case of heterogeneous treatment effects in the strata. For this reason, a plethora of borrowing methods have been suggested, i.e. statistical methods which dynamically decide on the amount of information that the different cohorts will share with one another.
The planning of basket trial designs implementing dynamic borrowing is complicated by the fact that their operating characteristics need to be tuned to the specific trial setting and assumed response scenarios. We suggest a framework for tuning basket trial designs, consisting of the choice of an optimization algorithm, a utility function as optimization target, and performance measures of interest. The presented utility functions aim at defining a trade-off between type-I error and power, either locally in the separate cohorts or globally across the trial as a whole. This way both Bayesian and frequentist methods for borrowing can be optimized with respect to frequentist performance measures, which allows for easy communication in clinical settings as well as objective comparison between different borrowing methods. In a comprehensive simulation study, we investigated the framework in the optimization of a Bayesian basket trial design suggested by Fujikawa et al. in 2020. The simulation results highlighted the benefit of optimizing performance measures across a range of possible outcome scenarios (from no stratum responding to all strata responding) and the need for adapting tuning parameters to the particular trial setting.
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