weights_mml {baskexact}  R Documentation 
Weights Based on the Marginal Maximum Likelihood
Description
Weights Based on the Marginal Maximum Likelihood
Usage
weights_mml(design, ...)
## S4 method for signature 'OneStageBasket'
weights_mml(
design,
n,
prune = FALSE,
lambda,
globalweight_fun = NULL,
globalweight_params = list(),
...
)
## S4 method for signature 'TwoStageBasket'
weights_mml(design, n, n1, ...)
Arguments
design 
An object of class 
... 
Further arguments. 
n 
The sample size per basket. 
prune 
Whether baskets with a number of responses below the
critical pooled value should be pruned before the final analysis.
If this is 
lambda 
The posterior probability threshold. See details for more information. 
globalweight_fun 
Which function should be used to calculate the global weights. 
globalweight_params 
A list of tuning parameters specific to

n1 
The sample size per basket for the interim analysis in case of a twostage design. 
Details
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
weightparameter given the dataset that information should be borrowed
from. However, since this can lead to nonsymmetric 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 sharingweight 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.
Value
A matrix including the weights of all possible pairwise outcomes.
Methods (by class)

weights_mml(OneStageBasket)
: Maximum marginal likelihood weights for a singlestage basket design 
weights_mml(TwoStageBasket)
: Maximum marginal likelihood weights for a twostage basket design
References
Gravestock, I., & Held, L. (2017). Adaptive power priors with empirical Bayes for clinical trials. Pharmaceutical statistics, 16(5), 349360.
Examples
design < setupOneStageBasket(k = 3, p0 = 0.2)
toer(design, n = 15, lambda = 0.99, weight_fun = weights_mml)