## Test for the Results of a Basket Trial

### Description

basket_test evaluates the results of a basket trial and calculates the posterior distributions with and without borrowing.

### Usage

basket_test(design, n, r, lambda, epsilon, tau, logbase = 2, prune, ...)

## S4 method for signature 'OneStageBasket'
basket_test(design, n, r, lambda, epsilon, tau, logbase = 2, prune, ...)


### Arguments

 design An object of class Basket created by setupBasket. n The sample size per basket. r The vector of observed responses. lambda The posterior probability threshold. See details for more information. epsilon A tuning parameter that determines the amount of borrowing. See details for more information. tau A tuning parameter that determines how similar the baskets have to be that borrowing occurs. See details for more information. logbase A tuning parameter that determines which logarithm base is used to compute the Jensen-Shannon divergence. See details for more information. prune Whether baskets with a number of responses below the critical pooled value should be pruned before the final analysis. ... Further arguments.

### Value

A list, including matrices of the weights that are used for borrowing, posterior distribution parameters for all baskets without and with borrowing, as well as the posterior probabilities for all baskets without and with borrowing.

### Methods (by class)

• OneStageBasket: Testing for a single-stage basket design.

### Examples

design <- setupOneStageBasket(k = 3, shape1 = 1, shape2 = 1, theta0 = 0.2)
basket_test(design = design, n = 24, r = c(5, 9, 10), lambda = 0.99,
epsilon = 1, tau = 0, logbase = 2, prune = FALSE)