GBOP2_maxP_dualE: PSOGO: Power maximizing design with dual boundaries

In GBOP2: Generalized Bayesian Optimal Phase II Design (G-BOP2)

PSOGO: Power maximizing design with dual boundaries

Description

This function implements PSOGO to find a power maximizing design with dual boundaries.

Arguments

design	fixed as "optimal", which can not be modified by user
nlooks	number of interim looks
p0	Null hypothesis response rate
p1	Alternative hypothesis response rate
err1	Type I error rate
nParallel	number of pso ensemble
minPower	power
totalPatients	total patients
Nmin_cohort1	minimum number of first cohort
Nmin_increase	minimum number of increase in each cohort
pso_method	"all" for using three distinct pso, otherwise indicate single pso method
seed	seed for pso
nSwarm	nSwarm for pso
maxIter	maxIter for pso

Details

Parallel computing is only used when the user explicitly sets nCore > 1. No more than 2 cores should be used unless the user is aware and permits it. The function defaults to sequential execution. If multiple analyses are planned, consider using init_cluster(nCore) and stop_cluster() manually to control the backend.

Value

A list on design parameters and operating characteristics

Examples

# init_cluster(2)
# GBOP2_maxP_dualE(
#   nlooks = 1, 
#   p0 = 0.2, 
#   p1 = 0.4, 
#   err1 = 0.05, 
#   minPower = 0.8, 
#   totalPatients = 26, 
#   Nmin_cohort1 = 10, 
#   Nmin_increase = 5, 
#   pso_method = "default", 
#   nParallel = 1, 
#   seed = 1024, 
#   nSwarm = 64, 
#   maxIter = 200
# )
# stop_cluster()  # Only if init_cluster() was used
#  
message("Run GBOP2_minSS_singleE() manually for real optimization.")

GBOP2 documentation built on April 11, 2025, 5:42 p.m.