### Description

OneStageBasket is an S4 class. An object of this class contains the most important design features of a single-stage basket trial.

### Details

This class implements a single-stage basket trial based on the design proposed by Fujikawa et al. In this design, at first separate posterior distributions are calculated for each basket based on a beta-binomial model. Information is then borrowed between baskets by calculating weights that reflect the similarity between the basket and computing posterior distributions for each basket where the parameters of the beta posterior are calculated as a weighted sum of the individual posterior distributions. The weight between two baskets i and j is found as (1 - JSD(i, j))^epsilon where JSD(i, j) is the Jensen-Shannon divergence between basket i and j. A small value of epsilon results in stronger borrowing also across baskets with heterogenous results. If epsilon is large then information is only borrowed between baskets with similar results. If a weight is smaller than tau it is set to 0, which results in no borrowing. If for a basket the posterior probability that \theta > theta0 is greater than lambda, then the null hypothesis is rejected.

Currently only common prior distributions and a common null hypothesis are supported.

### Slots

k

shape1

First common shape parameter of the beta prior.

shape2

Second common shape parameter of the beta prior.

theta0

A common probability under the null hypothesis.

### References

Fujikawa, K., Teramukai, S., Yokota, I., & Daimon, T. (2020). A Bayesian basket trial design that borrows information across strata based on the similarity between the posterior distributions of the response probability. Biometrical Journal, 62(2), 330-338.