check_mon_within {baskexact}  R Documentation 
Check WithinTrial Monotonicity
Description
Checks whether the withintrial monotonicity condition holds.
Usage
check_mon_within(design, ...)
## S4 method for signature 'OneStageBasket'
check_mon_within(
design,
n,
lambda,
weight_fun,
weight_params = list(),
globalweight_fun = NULL,
globalweight_params = list(),
details = TRUE,
...
)
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

globalweight_fun 
Which function should be used to calculate the global weights. 
globalweight_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. 
Details
check_mon_within
checks whether the withintrial
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 the null hypothesis of a basket is
rejected when there is at least one other basket with more observed
responses for which the null hypothesis cannot be 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
withintrial monotonicity condition is violated, a list of these cases and
their results are returned. If at least one tuning parameter is a vector,
then an array that shows for which combination of parameters the
withintrial monotonicity condition holds. In this case, the argument
details
is ignored.
Methods (by class)

check_mon_within(OneStageBasket)
: Withintrial 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
design < setupOneStageBasket(k = 4, shape1 = 1, shape2 = 1, p0 = 0.2)
check_mon_within(design = design, n = 24, lambda = 0.99,
weight_fun = weights_fujikawa, weight_params = list(epsilon = 0.5,
tau = 0), details = TRUE)
# Vectorized
check_mon_within(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)))