interim_postpred | R Documentation |
Conducts an interim analysis based on the posterior predictive probability.
interim_postpred(design, ...)
## S4 method for signature 'TwoStageBasket'
interim_postpred(
design,
n,
n1,
r1,
lambda,
weight_mat,
globalweight_fun = NULL,
globalweight_params,
prob_futstop = 0.1,
prob_effstop = 0.9,
...
)
design |
An object of class |
... |
Further arguments. |
n |
The sample size per basket. |
n1 |
The sample size per basket for the interim analysis in case of a two-stage design. |
r1 |
Vector of responses after the interim analysis. |
lambda |
The posterior probability threshold. See details for more information. |
weight_mat |
The matrix with all weights. Automatically calculated
in the functions to which |
globalweight_fun |
Which function should be used to calculate the global weights. |
globalweight_params |
A list of tuning parameters specific to
|
prob_futstop |
Probability cut-off for stopping for futility. |
prob_effstop |
Probability cut-off for stopping for efficacy. |
interim_postpred
conducts an interim analysis with possible
stop for efficacy and futility based on the posterior predictive probability.
If the posterior predictive probability is less than prob_fustop
the
basket is stopped for futility, if the posterior predictive probability is
greater than prob_effstop
the basket is stopped for efficacy. If
prob_fustop = 0
or prob_effstop = 1
then no futility-stop and
no efficacy stop is possible, respectively.
The function is generally not called by the user but passed to another
function such as toer
and pow
to specify which
interim analysis is conducted.
A vector with a length equal to the number of baskets with elements -1, 0 or 1 where -1 means stop for futility, 0 means continuation and 1 means stop for efficacy.
interim_postpred(TwoStageBasket)
: Interim analysis based on the posterior
predictive probabilty for two-stage basket designs.
design <- setupTwoStageBasket(k = 3, p0 = 0.2)
toer(design, n = 20, n1 = 10, lambda = 0.99, interim_fun = interim_postpred,
weight_fun = weights_fujikawa)
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