| Title: | Generalized Bayesian Optimal Phase II Design (G-BOP2) |
|---|---|
| Description: | Provides functions for implementing the Generalized Bayesian Optimal Phase II (G-BOP2) design using various Particle Swarm Optimization (PSO) algorithms, including: - PSO-Default, based on Kennedy and Eberhart (1995) <doi:10.1109/ICNN.1995.488968>, "Particle Swarm Optimization"; - PSO-Quantum, based on Sun, Xu, and Feng (2004) <doi:10.1109/ICCIS.2004.1460396>, "A Global Search Strategy of Quantum-Behaved Particle Swarm Optimization"; - PSO-Dexp, based on Stehlík et al. (2024) <doi:10.1016/j.asoc.2024.111913>, "A Double Exponential Particle Swarm Optimization with Non-Uniform Variates as Stochastic Tuning and Guaranteed Convergence to a Global Optimum with Sample Applications to Finding Optimal Exact Designs in Biostatistics"; - and PSO-GO. |
| Authors: | Xinying Fang [aut], Wanni Lei [aut, cre], Shouhao Zhou [aut], Ping-Yang Chen [aut], Ray-Bing Chen [aut], Weng Kee Wong [aut], J. Jack Lee [aut] |
| Maintainer: | Wanni Lei <[email protected]> |
| License: | GPL-2 |
| Version: | 0.1.4 |
| Built: | 2026-05-22 09:58:51 UTC |
| Source: | https://github.com/cran/GBOP2 |
This function implements PSOGO to find a power maximizing design with dual boundaries.
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 |
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.
A list on design parameters and operating characteristics
# 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 = 3, # seed = 1024, # nSwarm = 64, # maxIter = 200 # ) # stop_cluster() # Only if init_cluster() was used # message("Run GBOP2_minSS_singleE() manually for real optimization.")# 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 = 3, # seed = 1024, # nSwarm = 64, # maxIter = 200 # ) # stop_cluster() # Only if init_cluster() was used # message("Run GBOP2_minSS_singleE() manually for real optimization.")
This function implements PSOGO to find a power maximizing design with single boundary for futility.
GBOP2_maxP_singleE( nlooks = 1, p0 = 0.2, p1 = 0.4, err1 = 0.05, minPower = 0.8, totalPatients = 5, Nmin_cohort1 = 1, Nmin_increase = 1, pso_method = "default", nParallel = 3, seed = 1024, nSwarm = 64, maxIter = 200 )GBOP2_maxP_singleE( nlooks = 1, p0 = 0.2, p1 = 0.4, err1 = 0.05, minPower = 0.8, totalPatients = 5, Nmin_cohort1 = 1, Nmin_increase = 1, pso_method = "default", nParallel = 3, seed = 1024, nSwarm = 64, maxIter = 200 )
nlooks |
number of interim looks |
p0 |
Null hypothesis response rate |
p1 |
Alternative hypothesis response rate |
err1 |
Type I error rate |
minPower |
power |
totalPatients |
total number of 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 |
nParallel |
number of pso ensemble |
seed |
Random seed for reproducibility |
nSwarm |
nSwarm for pso |
maxIter |
maxIter for pso |
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.
A list on design parameters and operating characteristics
# init_cluster(2) # GBOP2_maxP_singleE( # 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 = 3, # seed = 1024, # nSwarm = 64, # maxIter = 200 # ) # stop_cluster() # Only if init_cluster() was used # message("Run GBOP2_minSS_singleE() manually for real optimization.")# init_cluster(2) # GBOP2_maxP_singleE( # 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 = 3, # seed = 1024, # nSwarm = 64, # maxIter = 200 # ) # stop_cluster() # Only if init_cluster() was used # message("Run GBOP2_minSS_singleE() manually for real optimization.")
This function implements PSOGO to find a power maximizing design with efficacy and toxicity boundaries.
design |
fixed as "optimal", cannot be modified by user |
pso_method |
method for single PSO, choose from "default", "quantum" or "dexp" |
nlooks |
number of interim looks |
skip_efficacy |
default is NULL, indicate skip efficacy as 1 and not skip as 0 in a vector |
skip_toxicity |
default is NULL, indicate skip toxicity as 1 and not skip as 0 in a vector |
totalPatients |
number of total patients |
Nmin_cohort1 |
maximum number of patients |
Nmin_increase |
minimum number of first cohort |
p01 |
H0 for efficacy |
p02 |
H0 for toxicity |
p03 |
H0 for Eff and Tox |
p11 |
H1 for efficacy |
p12 |
H1 for toxicity |
p13 |
H1 for Eff and Tox |
err_eff |
Type I error rate: Efficacious but toxic |
err_tox |
Type I error rate: Safe but futile |
err_all |
Type I error rate: Futile and toxic |
power_all |
power: Futile and toxic |
nSwarm |
nSwarm in PSO |
maxIter |
maxIter in PSO |
nParallel |
number of PSO ensemble |
seed |
Random seed for reproducibility |
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.
A list on design parameters and operating characteristics
# init_cluster(2) # GBOP2_maxP_TE( # design = "optimal", # nlooks = 1, # skip_efficacy = NULL, # skip_toxicity = NULL, # totalPatients = 50, # Nmin_cohort1 = 10, # Nmin_increase = 5, # p01 = 0.15, # p02 = 0.16, # p03 = 0.024, # p11 = 0.4, # p12 = 0.08, # p13 = 0.032, # err_eff = 1, # err_tox = 1 , # err_all = 0.1, # power_all = 0.8, # nParallel = 3, # seed = 5321, # pso_method = "default", # nSwarm = 32, # maxIter = 100) # stop_cluster() # Only if init_cluster() was used # message("Run GBOP2_minSS_singleE() manually for real optimization.")# init_cluster(2) # GBOP2_maxP_TE( # design = "optimal", # nlooks = 1, # skip_efficacy = NULL, # skip_toxicity = NULL, # totalPatients = 50, # Nmin_cohort1 = 10, # Nmin_increase = 5, # p01 = 0.15, # p02 = 0.16, # p03 = 0.024, # p11 = 0.4, # p12 = 0.08, # p13 = 0.032, # err_eff = 1, # err_tox = 1 , # err_all = 0.1, # power_all = 0.8, # nParallel = 3, # seed = 5321, # pso_method = "default", # nSwarm = 32, # maxIter = 100) # stop_cluster() # Only if init_cluster() was used # message("Run GBOP2_minSS_singleE() manually for real optimization.")
This function implements PSOGO to find an optimal or minimax design with dual boundaries.
GBOP2_minSS_dualE( design = "optimal", unified.u = unified.u, weight = 1, nlooks = 1, p0 = 0.2, p1 = 0.4, err1 = 0.05, minPower = 0.8, maxPatients = 5, Nmin_cohort1 = 1, Nmin_increase = 1, pso_method = "default", nParallel = 3, seed = 123, nSwarm = 64, maxIter = 200 )GBOP2_minSS_dualE( design = "optimal", unified.u = unified.u, weight = 1, nlooks = 1, p0 = 0.2, p1 = 0.4, err1 = 0.05, minPower = 0.8, maxPatients = 5, Nmin_cohort1 = 1, Nmin_increase = 1, pso_method = "default", nParallel = 3, seed = 123, nSwarm = 64, maxIter = 200 )
design |
choose from "optimal", "minimax", or "unified" |
unified.u |
specify when design = "unified", u in zero to one |
weight |
weight of sample size under null |
nlooks |
number of interim looks |
p0 |
Null hypothesis response rate |
p1 |
Alternative hypothesis response rate |
err1 |
Type I error rate |
minPower |
power |
maxPatients |
maximum number of 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 |
nParallel |
number of pso ensemble |
seed |
seed for pso |
nSwarm |
nSwarm for pso |
maxIter |
maxIter for pso |
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.
A list on design parameters and operating characteristics
# init_cluster(2) # GBOP2_minSS_dualE( # design = "optimal", # unified.u = unified.u, # nlooks = 1, # p0 = 0.2, # p1 = 0.4, # err1 = 0.05, # minPower = 0.8, # weight = 1, # maxPatients = 25, # Nmin_cohort1 = 10, # Nmin_increase = 5, # pso_method = "default", # nParallel = 3, # seed = 123, # nSwarm = 64, # maxIter = 200 # ) # stop_cluster() # Only if init_cluster() was used # message("Run GBOP2_minSS_dualE() manually for real optimization.")# init_cluster(2) # GBOP2_minSS_dualE( # design = "optimal", # unified.u = unified.u, # nlooks = 1, # p0 = 0.2, # p1 = 0.4, # err1 = 0.05, # minPower = 0.8, # weight = 1, # maxPatients = 25, # Nmin_cohort1 = 10, # Nmin_increase = 5, # pso_method = "default", # nParallel = 3, # seed = 123, # nSwarm = 64, # maxIter = 200 # ) # stop_cluster() # Only if init_cluster() was used # message("Run GBOP2_minSS_dualE() manually for real optimization.")
This function implements PSOGO to find an optimal or minimax design with single boundary for futility.
GBOP2_minSS_singleE( design = "optimal", unified.u = 1, weight = 1, nlooks = 2, p0 = 0.2, p1 = 0.4, err1 = 0.05, minPower = 0.8, maxPatients = 5, Nmin_cohort1 = 1, Nmin_increase = 1, pso_method = "default", nParallel = 3, seed = 456, nSwarm = 64, maxIter = 200 )GBOP2_minSS_singleE( design = "optimal", unified.u = 1, weight = 1, nlooks = 2, p0 = 0.2, p1 = 0.4, err1 = 0.05, minPower = 0.8, maxPatients = 5, Nmin_cohort1 = 1, Nmin_increase = 1, pso_method = "default", nParallel = 3, seed = 456, nSwarm = 64, maxIter = 200 )
design |
choose from "optimal", "minimax", or "unified" |
unified.u |
specify when design = "unified", u in zero to one |
weight |
weight of sample size under null |
nlooks |
number of interim looks |
p0 |
Null hypothesis response rate |
p1 |
Alternative hypothesis response rate |
err1 |
Type I error rate |
minPower |
power |
maxPatients |
maximum number of 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 |
nParallel |
number of pso ensemble |
seed |
Random seed for reproducibility |
nSwarm |
nSwarm for pso |
maxIter |
maxIter for pso |
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.
A list on design parameters and operating characteristics
# init_cluster(2) # GBOP2_minSS_singleE( # design = "optimal", # unified.u = 1, # nlooks = 1, # p0 = 0.2, # p1 = 0.4, # err1 = 0.05, # minPower = 0.8, # weight = 1, # maxPatients = 25, # Nmin_cohort1 = 10, # Nmin_increase = 5, # pso_method = "default", # nParallel = 3, # seed = 1024, # nSwarm = 64, # maxIter = 200 # ) # stop_cluster() # Only if init_cluster() was used # message("Run GBOP2_minSS_singleE() manually for real optimization.")# init_cluster(2) # GBOP2_minSS_singleE( # design = "optimal", # unified.u = 1, # nlooks = 1, # p0 = 0.2, # p1 = 0.4, # err1 = 0.05, # minPower = 0.8, # weight = 1, # maxPatients = 25, # Nmin_cohort1 = 10, # Nmin_increase = 5, # pso_method = "default", # nParallel = 3, # seed = 1024, # nSwarm = 64, # maxIter = 200 # ) # stop_cluster() # Only if init_cluster() was used # message("Run GBOP2_minSS_singleE() manually for real optimization.")
This function implements PSOGO to find an optimal or minimax design with efficacy and toxicity boundaries.
GBOP2_minSS_TE( design = "optimal", unified.u = 1, nlooks = 1, skip_efficacy = NULL, skip_toxicity = NULL, maxPatients = 26, Nmin_cohort1 = 13, Nmin_increase = 13, p01 = 0.3, p02 = 0.4, p03 = 0.2, p11 = 0.6, p12 = 0.2, p13 = 0.15, err_eff = 0.1, err_tox = 0.1, err_all = 0.05, power_all = 0.8, pso_method = "all", nParallel = 3, seed = 1324, nSwarm = 32, maxIter = 100 )GBOP2_minSS_TE( design = "optimal", unified.u = 1, nlooks = 1, skip_efficacy = NULL, skip_toxicity = NULL, maxPatients = 26, Nmin_cohort1 = 13, Nmin_increase = 13, p01 = 0.3, p02 = 0.4, p03 = 0.2, p11 = 0.6, p12 = 0.2, p13 = 0.15, err_eff = 0.1, err_tox = 0.1, err_all = 0.05, power_all = 0.8, pso_method = "all", nParallel = 3, seed = 1324, nSwarm = 32, maxIter = 100 )
design |
choose from "optimal", "minimax", or "unified" |
unified.u |
specify when design = "unified", u in zero to one |
nlooks |
number of interim looks |
skip_efficacy |
default is NULL, indicate skip efficacy as 1 and not skip as 0 in a vector |
skip_toxicity |
default is NULL, indicate skip toxicity as 1 and not skip as 0 in a vector |
maxPatients |
maximum number of patients |
Nmin_cohort1 |
minimum number of first cohort |
Nmin_increase |
minimum number of increase in each cohort |
p01 |
H0 for efficacy |
p02 |
H0 for toxicity |
p03 |
H0 for Eff and Tox |
p11 |
H1 for efficacy |
p12 |
H1 for toxicity |
p13 |
H1 for Eff and Tox |
err_eff |
Type I error rate: Efficacious but toxic |
err_tox |
Type I error rate: Safe but futile |
err_all |
Type I error rate: Futile and toxic |
power_all |
power: Futile and toxic |
pso_method |
"all" for using three distinct pso, otherwise indicate single pso method |
nParallel |
number of pso ensemble |
seed |
Random seed for reproducibility |
nSwarm |
nSwarm in PSO |
maxIter |
maxIter in PSO |
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.
A list on design parameters and operating characteristics
# init_cluster(2) # GBOP2_minSS_TE( # design = "optimal", # unified.u = 1, # nlooks = 1, # skip_efficacy = NULL, # skip_toxicity = NULL, # maxPatients = 25, # Nmin_cohort1 = 10, # Nmin_increase = 5, # p01 = 0.3, # p02 = 0.4, # p03 = 0.2, # p11 = 0.6, # p12 = 0.2, # p13 = 0.15, # err_eff = 0.1, # err_tox = 0.1, # err_all = 0.05, # power_all = 0.8, # pso_method = "default", # nParallel = 3, # seed = 5321, # nSwarm = 64, # maxIter = 100 # ) # stop_cluster() # Only if init_cluster() was used # message("Run GBOP2_minSS_singleE() manually for real optimization.")# init_cluster(2) # GBOP2_minSS_TE( # design = "optimal", # unified.u = 1, # nlooks = 1, # skip_efficacy = NULL, # skip_toxicity = NULL, # maxPatients = 25, # Nmin_cohort1 = 10, # Nmin_increase = 5, # p01 = 0.3, # p02 = 0.4, # p03 = 0.2, # p11 = 0.6, # p12 = 0.2, # p13 = 0.15, # err_eff = 0.1, # err_tox = 0.1, # err_all = 0.05, # power_all = 0.8, # pso_method = "default", # nParallel = 3, # seed = 5321, # nSwarm = 64, # maxIter = 100 # ) # stop_cluster() # Only if init_cluster() was used # message("Run GBOP2_minSS_singleE() manually for real optimization.")
Returns the current parallel cluster object, if initialized.
get_cluster()get_cluster()
A cluster object or NULL.
Creates and registers a parallel backend using the specified number of cores. Falls back to sequential execution if nCore <= 1.
init_cluster(nCore = 2)init_cluster(nCore = 2)
nCore |
Number of cores to use (default is 2). |
Stops the currently running parallel cluster and reverts to sequential execution.
stop_cluster()stop_cluster()
Summary function Summary function for gbop2 objects
## S3 method for class 'gbop2' summary(object, ...)## S3 method for class 'gbop2' summary(object, ...)
object |
GBOP2_maxP_dualE GBOP2_maxP_singleE GBOP2_maxP_TE GBOP2_minSS_dualE GBOP2_minSS_singleE GBOP2_minSS_TE |
... |
ignored arguments |
A summary table
design <- GBOP2_minSS_singleE( design = "optimal", unified.u = 1, nlooks = 1, p0 = 0.2, p1 = 0.4, err1 = 0.05, minPower = 0.8, weight = 1, maxPatients = 25, Nmin_cohort1 = 10, Nmin_increase = 5, pso_method = "default", nParallel = 1, seed = 1024, nSwarm = 64, maxIter = 200 ) summary(design)design <- GBOP2_minSS_singleE( design = "optimal", unified.u = 1, nlooks = 1, p0 = 0.2, p1 = 0.4, err1 = 0.05, minPower = 0.8, weight = 1, maxPatients = 25, Nmin_cohort1 = 10, Nmin_increase = 5, pso_method = "default", nParallel = 1, seed = 1024, nSwarm = 64, maxIter = 200 ) summary(design)