Speaker
Description
Basket trials examine the efficacy of a single intervention simultaneously in several patient subgroups. They are currently mostly applied in oncology, where the subgroup assignment is based on medical characteristics such as a common biomarker. This can result in small sample sizes within subgroups that are also likely to differ. Several designs for the analysis of basket trials have been proposed in the literature that share information across subgroups to increase power. Many designs utilise Bayesian methods, such as hierarchical modelling or model averaging. A recently proposed design based on power priors uses empirical Bayes methods to increase the computational efficacy compared to fully Bayesian designs. The design incorporates data from all subgroups using a weighted likelihood that shares information according to the similarity of the subgroups. However, if the sample sizes differ, there is a risk that the information from the small subgroups will be overlaid by that from the large subgroups.
We extend the power prior design by applying a weighting method, previously suggested for sharing information from historical data, that accounts for unequal sample sizes by limiting the amount of information shared between subgroups. The new weights take the pairwise ratio of subgroup sample sizes into account, such that the effective sample size that is shared from a subgroup cannot exceed the sample size of the subgroup of interest. Using a simulation study, we systematically compare the power prior design with previously suggested weights and the new information-limiting weighting method to other Bayesian basket trial designs with respect to the expected number of correct decisions, type 1 error rates and power. We consider a range of different scenarios with different true response probabilities and sample sizes across subgroups.
The results of the simulation study show that the new information-limiting weights improve the results of the original power prior design. In terms of the expected number of correct decisions, the improved power prior design performs slightly better than the competing designs in all sample size scenarios. In scenarios with some active and some inactive baskets, the inflation of the type 1 error rates is less severe than with unlimited sharing.
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