Weights Based on the Marginal Maximum Likelihood
weights_mml(design, ...)# S4 method for OneStageBasket
weights_mml(
design,
n,
prune = FALSE,
lambda,
globalweight_fun = NULL,
globalweight_params = list(),
...
)
# S4 method for TwoStageBasket
weights_mml(design, n, n1, ...)
A matrix including the weights of all possible pairwise outcomes.
An object of class Basket created by
setupOneStageBasket or setupTwoStageBasket.
Further arguments.
The sample size per basket.
Whether baskets with a number of responses below the
critical pooled value should be pruned before the final analysis.
If this is TRUE then lambda is also required and
if globalweight_fun is not NULL then
globalweight_fun and globalweight_params are also used.
The posterior probability threshold. See details for more information.
Which function should be used to calculate the global weights.
A list of tuning parameters specific to
globalweight_fun.
The sample size per basket for the interim analysis in case of a two-stage design.
weights_mml(OneStageBasket): Maximum marginal likelihood weights for a
single-stage basket design
weights_mml(TwoStageBasket): Maximum marginal likelihood weights for a
two-stage basket design
weights_mml calculates the weights based on the marginal
maximum likelihood approach by Gravestock & Held (2017). In this approach,
the weight is found as the maximum of the marginal likelihood of the
weight-parameter given the dataset that information should be borrowed
from. However, since this can lead to non-symmetric weights (meaning that
the amount of information that data set 1 borrows from data set 2 is
generally not identical to the information data set 2 borrows from data set
1), a symmetrised version is used here: For the sharing-weight of
Basket 1 and Basket 2 the MML is calculted two times - once conditional
on the data of Basket 1 and once conditional on the data of Basket 2.
The mean of these two weights is then used, resulting in symmetrical
sharing.
Gravestock, I., & Held, L. (2017). Adaptive power priors with empirical Bayes for clinical trials. Pharmaceutical statistics, 16(5), 349-360.
design <- setupOneStageBasket(k = 3, p0 = 0.2)
toer(design, n = 15, lambda = 0.99, weight_fun = weights_mml)
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