| dabcd_max_power {RARtrials} | R Documentation |
Allocation Probabilities Using Doubly Adaptive Biased Coin Design with Maximal Power Strategy for Binary Endpoint
Description
dabcd_max_power can be used for doubly adaptive biased coin design with maximal power
strategy for binary outcomes, targeting generalized Neyman allocation and generalized RSIHR allocation. The return
of this function is a vector of allocation probabilities to each arm, with the pre-specified number of participants in the trial.
Usage
dabcd_max_power(NN, Ntotal1, armn, BB, type, dabcd = FALSE, gamma = 2)
Arguments
NN |
a vector representing the number of participants with success results for each arm estimated from the current data. |
Ntotal1 |
a vector representing the total number of participants for each arm estimated from the current data. |
armn |
number of total arms in the trial. |
BB |
the minimal allocation probability for each arm, which is within the
range of |
type |
allocation type, with choices from 'RSIHR' and 'Neyman'. |
dabcd |
an indicator of whether to apply Hu & Zhang's formula ((Hu and Zhang 2004)), with choices from FALSE and TRUE. TRUE represents allocation probabilities calculated using Hu & Zhang's formula; FALSE represents allocation probabilities calculated before applying Hu & Zhang's formula. Default value is set to FALSE. |
gamma |
tuning parameter in Hu & Zhang's formula ((Hu and Zhang 2004)). When |
Details
The function simulates allocation probabilities for doubly adaptive biased coin design with maximal power strategy targeting
generalized Neyman allocation with 2-5 arms which is provided in (Tymofyeyev et al. 2007) or
generalized RSIHR allocation with 2-3 arms which is provided in (Jeon and Feifang 2010), with modifications for typos
in (Sabo and Bello 2016). All of those methods are not smoothed. The output of this function is based on Hu \& Zhang's formula (Hu and Zhang 2004).
With more than two armd the one-sided nominal level of each test is alphaa divided by arm*(arm-1)/2; a Bonferroni correction.
Value
A vector of allocation probabilities to each arm.
Author(s)
Chuyao Xu, Thomas Lumley, Alain Vandal
References
Hu F, Zhang L (2004). “Asymptotic Properties of Doubly Adaptive Biased Coin Designs for Multitreatment Clinical Trials.” The Annals of Statistics, 32(1), 268–301. Tymofyeyev Y, Rosenberger WF, Hu F (2007). “Implementing Optimal Allocation in Sequential Binary Response Experiments.” Journal of the American Statistical Association, 102(477), 224-234. doi:10.1198/016214506000000906. Jeon Y, Feifang H (2010). “Optimal Adaptive Designs for Binary Response Trials With Three Treatments.” Statistics in Biopharmaceutical Research, 2, 310-318. doi:10.1198/sbr.2009.0056. Sabo R, Bello G (2016). “Optimal and lead-in adaptive allocation for binary outcomes: a comparison of Bayesian methodologies.” Communications in Statistics - Theory and Methods, 46.
Examples
dabcd_max_power(NN=c(54,67,85,63,70),Ntotal1=c(100,88,90,94,102),armn=5,BB=0.2, type='Neyman')
dabcd_max_power(NN=c(54,67,85,63),Ntotal1=c(100,88,90,94),armn=4,BB=0.2, type='Neyman')