Reach to the bus stop with high wall (Bus stop are on both sides) and reset your car before touching bus stop boundary with car so that your car can slide in bus stop without break the bus stop.
In the sequential analysis of data, both Bayesian and frequentist methods often make use of stopping rules or stopping boundaries.
Simulation is used to assess the accuracy of both methods for various stopping boundaries and numbers of interim analyses.
In this manuscript, we propose a simple method of searching stopping boundaries of both hypotheses simultaneously that satisfy α and β constrains using binomial distribution.
Conventional group-sequential multi-arm multi-stage (MAMS) designs use pre-specified stopping boundaries and treatment selection rules to determine if experimental treatments should be dropped.
To allow for unknown variance, previous research explored the performance of t-test statistics, coupled with a quantile substitution procedure for modifying the stopping boundaries, at controlling the familywise error-rate to the nominal level.
In this study, we propose a simulated annealing (SA -based deSA -basedptimize the early stopping boundesign and minimize toe expected sample size foptimize-stheearlycontinuoustoppingring single-arm trials.
Here, we discuss an alternative method based on Monte Carlo simulation that allows the group size and stopping boundaries of a multi-arm multi-stage t-test to be optimised, according to some nominated optimality criteria.
A characterization of optimal stopping boundaries (ymapsto g_ y)) in terms of the maximal solution of a nonlinear first-order differential equation for a general class of diffusion processes is given in Peskir [3].
However three issues remain unclear to investigators: 1) among several BRAR algorithms, how to choose one for the specific trial setting; 2) when to start and how frequently to update the allocation ratio; and 3) how to choose the interim analyses stopping boundaries to preserve the type 1 error.
