## Check Between-Trial Monotonicity

### Description

Checks whether the between-trial monotonicity condition holds.

### Usage

check_mon_between(
design,
n,
lambda,
epsilon,
tau,
logbase = 2,
prune,
details,
...
)

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


### Arguments

 design An object of class Basket created by setupBasket. n The sample size per basket. 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. details Whether the cases where the monotonicity condition is violated should be returned, in case there are any. ... Further arguments.

### Details

check_mon_between checks whether the between-trial monotonicity condition holds. For a single-stage design with equal prior distributions and equal sample sizes for each basket this condition states that there are no cases where at least one null hypothesis is rejected when when there is a case with an equal or higher number of responses in each basket for which no null hypothesis is rejected.

If prune = TRUE then the baskets with an observed number of baskets smaller than the pooled critical value are not borrowed from. The pooled critical value is the smallest integer c for which all null hypotheses can be rejected if the number of responses is exactly c for all baskets.

This method is implemented for the class OneStageBasket.

### Value

If details = FALSE then only a logical value is returned. If details = TRUE then if there are any cases where the between-trial monotonicity condition is violated, a list of theses cases and their results are returned.

### Methods (by class)

• OneStageBasket: Between-trial monotonicity condition for a single-stage design.

### Examples

design <- setupOneStageBasket(k = 4, shape1 = 1, shape2 = 1, theta0 = 0.2)
check_mon_between(design = design, n = 24, lambda = 0.99, epsilon = 3,
tau = 0, prune = FALSE, details = TRUE)