check_mon_between {baskexact}  R Documentation 
Check BetweenTrial Monotonicity
Description
Checks whether the betweentrial monotonicity condition holds.
Usage
check_mon_between(design, ...)
## S4 method for signature 'OneStageBasket'
check_mon_between(
design,
n,
lambda,
weight_fun,
weight_params = list(),
details = TRUE,
globalweight_fun = NULL,
globalweight_params = list(),
...
)
Arguments
design 
An object of class 
... 
Further arguments. 
n 
The sample size per basket. 
lambda 
The posterior probability threshold. See details for more information. 
weight_fun 
Which function should be used to calculate the pairwise weights. 
weight_params 
A list of tuning parameters specific to

details 
Whether the cases where the monotonicity condition is violated should be returned, in case there are any. 
globalweight_fun 
Which function should be used to calculate the global weights. 
globalweight_params 
A list of tuning parameters specific to

Details
check_mon_between
checks whether the betweentrial
monotonicity condition holds. For a singlestage 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.
The function is vectorized, such that vectors can be specified in
weight_params
and globalweight_params
.
Value
If details = FALSE
then only a logical value is returned.
If details = TRUE
then if there are any cases where the
betweentrial monotonicity condition is violated, a list of theses cases
and their results are returned.
Methods (by class)

check_mon_between(OneStageBasket)
: Betweentrial monotonicity condition for a singlestage design.
References
Baumann, L., Krisam, J., & Kieser, M. (2022). Monotonicity conditions for avoiding counterintuitive decisions in basket trials. Biometrical Journal, 64(5), 934947.
Examples
design < setupOneStageBasket(k = 4, shape1 = 1, shape2 = 1, p0 = 0.2)
# Without vectorization, with details
check_mon_between(design = design, n = 24, lambda = 0.99,
weight_fun = weights_fujikawa, weight_params = list(epsilon = 3,
tau = 0), details = TRUE)
# Vectorized
check_mon_between(design = design, n = 24, lambda = 0.99,
weight_fun = weights_fujikawa,
weight_params = list(epsilon = c(0.5, 1), tau = c(0, 0.2, 0.3)),
globalweight_fun = globalweights_fix,
globalweight_params = list(w = c(0.5, 0.7)))