SimulateEKOAD: Simulate single-arm binary endpoint two-stage adaptive...

In arsenionhacolo/InferenceBEAGSD: Inference for Binary Endpoint Adaptive Group-Sequential Designs

Description

SimulateEKOAD Simulate trials following designs similar to that of Englert and Kieser(2013)'s.

Usage

SimulateEKOAD(replicates, dsgn, newpi1 = NULL, seed = NULL,
  deleteOld = TRUE)

Arguments

replicates	Number of trials to be simulated.
dsgn	Dataframe containing one of the designs in EKOADwn.
newpi1	New response rate under the alternative hypothesis used to simulate trials. If NULL (default), the one from the design is used.
seed	The seed for random number generator. If NULL (default), no seed is set and , hence, results are not reproducible.
deleteOld	If TRUE (default), the simulation sub-directory is cleared before simulations start.

Details

The original designs (like the ones in EKOptAdaptDesigns) must be pre-processed using the function dsgnPrep to get extra information like the designs in EKOADwn.

Value

Simulated trials are saved in the sub-directory ./SimulatedTrials.

Author(s)

Arsenio Nhacolo

References

Englert, S. and Kieser, M. Optimal adaptive two-stage designs for phase II cancer clinical trials. Biometrical Journal, 2013.

See Also

EKOptAdaptDesigns, EKOADwn.

arsenionhacolo/InferenceBEAGSD documentation built on May 9, 2019, 4:10 a.m.