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Generalized Bayesian Optimal Phase II Design (G-BOP2)
In GBOP2: Generalized Bayesian Optimal Phase II Design (G-BOP2)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
`%operator%` <- GBOP2:::`%operator%` # triple-colon to access non-exported object
Installation
The R package GBOP2 is available for free on the Comprehensive R Archive Network (CRAN). It offers functions for implementing G-BOP2 design with various Particle Swarm Optimization (PSO) algorithms (Kennedy \& Eberhart 1995), including PSO-Default (Kennedy \& Eberhart, 1995), PSO-Quantum (Sun, Xu \& Feng 2004), PSO-DEXP (Stehl´ık et al. 2024), and PSO-GO. The G-BOP2 design includes two objectives, one is the sample size minimization, and the other is the power maximization. Parallel computing is only used when the user explicitly sets nCore > 1. The function defaults to sequential execution. If you plan to run parallel computing, you need to manually controlling the parallel backend using init_cluster(nCore) and stop_cluster(). The parallel backend will be automatically stopped when a function completes, so you will need to set nCore again each time before running the function. You can load the package using the following command:
#install.packages("GBOP2")
library(GBOP2)
Summary of functions
library(knitr)
# Create the data frame
data <- data.frame(
Category = c("Sample Size Minimization", "Power Maximization", ""),
`Efficacy - Single Boundary` = c("GBOP2_minSS_singleE", "GBOP2_maxP_singleE", ""),
`Efficacy - Dual Boundary` = c("GBOP2_minSS_dualE", "GBOP2_maxP_dualE", ""),
`Toxicity and Efficacy` = c("GBOP2_minSS_TE", "GBOP2_maxP_TE", "")
)
# Render the table
kable(data, align = "c", col.names = c("", "Efficacy - Single Boundary", "Efficacy - Dual Boundary", "Toxicity and Efficacy"))
Study Objective: sample size minimization
Efficacy: single boundary for futility
Objective function $L$:
link = system.file("Flowchart", "mss.png", package = "GBOP2")
knitr::include_graphics(link)
The parameters $u$ and $w$ are in the range $[0,1]$ and represent the pre-assigned weights for the study sample size $N$ and how it is distributed under the null hypothesis $H_0$ and the alternative hypothesis $H_1$, respectively. The score $g$ refers to some specified function on the sample size variable N and takes the unit-bounded value in $[0,1]$. The parameters $\alpha$ and $\beta$ refer to the Type I and Type II error. The constants $\alpha_0$ and $\beta_0$ refer to the nominal Type I and Type II error rates, while $n_{\text{max}}$ is the maximum allowable sample size. While $w$ is often set to 0, 1, or 1/2 in optimal designs to indicate diverse trial-specific considerations on $H_0$ and $H_1$, we recommend $w= 2/3$ to reflect the observation that only 1/3 of phase II studies successfully transit to phase III, suggesting empirically 2/3 chance in favor of $H_0$.
For example:
-
Simon's two stage optimal design (Simon 1989):
$$
g(N \mid H_.) = E(N \mid H_.)/n, u = 0, w = 1
$$
-
Simon's two stage minimax design (Simon 1989):
$$
g(N \mid H_.) = E(N \mid H_.)/n, u = 1, w =1
$$
-
Fleming 1982
$$
g(N \mid H_.) = E(N \mid H_.)/n, u = 0, w = 1
$$
The following examples illustrate the different designs identified by the PSO-GO algorithm. Note that the PSO-GO algorithm guarantees convergence to the global optimum almost surely when using a large nParallel value. If the global optimum is not reached, try increasing the nParallel value or running the algorithm again with different seeds.
The GBOP2_minSS_single function is designed to identify the optimal G-BOP2 design for single futility boundary. The optimal G-BOP2 design aims to minimize the sample size target specified in the objective function among the designs that satisfy the pre-specified nominal Type I error and minimum statistical power. Parameters such as nlooks, weight, maxPatients, Nmin_cohort1, and Nmin_increase provide users with more flexibility to adjust the number of interim looks, the weight of the sample size under the null hypothesis, and other design characteristics. When users choose pso_method equals "all", GBOP2_minSS_single function conducts PSO_Ensemble in parallel by indicating the number of parallel through nParallel option. Otherwise, users can specify the pso_method option as default, quantum, or dexp to conduct a single PSO algorithm. The following example show the optimal 3-stage G-BOP2 optimal design identified by the PSO-GO with $u=1$, $w=1$.
## PSO-GO
init_cluster(5) ## Initialize 5 parallel cluster
optimal_GO_single <- GBOP2_minSS_singleE(
design = "optimal", ## "optimal" or "minimax", "unified"
unified.u = 1, ## specify when design = "unified", u in [0, 1]
weight = 1, ## weight of sample size under null, w in [0, 1]
nlooks = 2, ## number of interim looks. For 3-stage design, nlooks = 2.
p0 = 0.2, ## response rate in null hypothesis
p1 = 0.4, ## response rate in alternative hypothesis
err1 = 0.05, ## Type Ierror
minPower = 0.8, ## power
maxPatients = 50, ## maximum number of patients
Nmin_cohort1 = 5, ## minimum number of first cohort
Nmin_increase = 5, ## minimum number of increase in each cohort
pso_method = "all", ## choose from "all", "default", "quantum" or "dexp"
nParallel = 3, ## number of PSO-ensemble, only effective when pso_method = "all"
seed = 456,
nSwarm = 64, ## nSwarm in PSO
maxIter = 200) ## maxIter in PSO
link = system.file("Flowchart", "progressbar.png", package = "GBOP2")
knitr::include_graphics(link)
## PSO-GO
init_cluster(5) ## Initialize 5 parallel cluster
optimal_GO_single <- GBOP2_minSS_singleE(
design = "optimal", ## "optimal" or "minimax", "unified"
unified.u = 1, ## specify when design = "unified", u in [0, 1]
weight = 1, ## weight of sample size under null, w in [0, 1]
nlooks = 2, ## number of interim looks. For 3-stage design, nlooks = 2.
p0 = 0.2, ## response rate in null hypothesis
p1 = 0.4, ## response rate in alternative hypothesis
err1 = 0.05, ## Type Ierror
minPower = 0.8, ## power
maxPatients = 50, ## maximum number of patients
Nmin_cohort1 = 5, ## minimum number of first cohort
Nmin_increase = 5, ## minimum number of increase in each cohort
pso_method = "all", ## choose from "all", "default", "quantum" or "dexp"
nParallel = 3, ## number of PSO-ensemble, only effective when pso_method = "all"
seed = 456,
nSwarm = 64, ## nSwarm in PSO
maxIter = 200) ## maxIter in PSO
Interpretation:
The GBOP2_minSS_singleE function successfully identified an optimal three-stage design with a power of over 80% and a Type I error rate of less than 5% . The optimized interim times are 8, 22, and 38 respectively, and the optimized stopping boundaries for each stage are 1, 5, and 11. The trial will be terminated and the drug will be considered not promising if the observed number of responses at any stage is less than or equal to the corresponding stopping boundary. Otherwise, the drug will be declared promising. The parameters section provides key values used in determining the optimal boundaries.
summary(optimal_GO_single)
Efficacy: dual boundary for futility monitoring and superiority detecting
The GBOP2_minSS_dual function is designed to identify the optimal and minimax designs for dual futility boundaries, which meets the pre-specified Type I and Type II error rates by using PSO_GO algorithm. When pso_method equals "all", GBOP2_minSS_dual function conducts PSO_Ensemble in parallel, otherwise, users can specify the pso_method option as default, quantum, or dexp to conduct a single PSO algorithm. The following example show the optimal design for dual futility boundary identified by the PSO-GO.
## PSO-GO
init_cluster(5) ## Initialize 5 parallel cluster
optimal_GO_dual <- GBOP2_minSS_dualE(
design = "optimal", ## choose from "optimal", "minimax" and "unified"
unified.u = 1, ## specify when design = "unified", u in [0, 1]
weight = 0.5, ## weight of sample size under null
nlooks = 1, ## number of interim looks (R)
p0 = 0.2, ## response rate in null hypothesis
p1 = 0.4, ## response rate in alternative hypothesis
err1 = 0.05, ## Type I error
minPower = 0.8, ## power
maxPatients = 50, ## maximum number of patients
Nmin_cohort1 = 10, ## minimum number of first cohort
Nmin_increase = 5, ## minimum number of increase in each cohort
pso_method = "all", ## choose from "all", ""default", "quantum" or "dexp"
nParallel = 3, ## number of PSO_ensemble
seed = 123,
nSwarm = 64, ## nSwarm in PSO
maxIter = 400) ## maxIter in PSO
link = system.file("Flowchart", "progressbar.png", package = "GBOP2")
knitr::include_graphics(link)
## PSO-GO
init_cluster(5) ## Initialize 5 parallel cluster
optimal_GO_dual <- GBOP2_minSS_dualE(
design = "optimal", ## choose from "optimal", "minimax" and "unified"
unified.u = 1, ## specify when design = "unified", u in [0, 1]
weight = 0.5, ## weight of sample size under null
nlooks = 1, ## number of interim looks (R)
p0 = 0.2, ## response rate in null hypothesis
p1 = 0.4, ## response rate in alternative hypothesis
err1 = 0.05, ## Type I error
minPower = 0.8, ## power
maxPatients = 50, ## maximum number of patients
Nmin_cohort1 = 10, ## minimum number of first cohort
Nmin_increase = 5, ## minimum number of increase in each cohort
pso_method = "all", ## choose from "all", ""default", "quantum" or "dexp"
nParallel = 3, ## number of PSO_ensemble
seed = 123,
nSwarm = 64, ## nSwarm in PSO
maxIter = 400 ## maxIter in PSO
)
Interpretation:
The GBOP2_minSS_dualE function successfully identified an optimal two-stage design with a power exceeding 80% and a Type I error rate below 5%. The optimized interim times are 14 and 37, respectively. The stopping boundaries for futility are 3 and 11, and the boundaries for efficacy are 7 and 12.The trial will be terminated for futility, and the drug will be considered not promising if the observed number of responses is $≤$ 3 in the first stage or $≤$ 11 in the second stage. Conversely, the trial will be terminated for efficacy, and the drug will be considered promising if the observed number of responses is $≥$ 7 in the first stage or $≥$ 12 in the second stage.The parameters section provides key values used in determining the optimal boundaries.
summary(optimal_GO_dual)
Toxicity and Efficacy: joint safety and futility monitoring
The GBOP2_minSS_TE function is designed to identify the optimal and minimax designs for toxicity and efficacy boundaries, which meets the pre-specified Type I and Type II error rates by using PSO_GO algorithm. When pso_method equals "all", GBOP2_minSS_dual function conducts PSO_Ensemble in parallel, otherwise, users can specify the pso_method option as default, quantum, or dexp to conduct a single PSO algorithm. The following example show the optimal design for dual futility boundaries identified by the PSO-GO.
init_cluster(5) ## Initialize 5 parallel cluster
Optimal_GO_TE <- GBOP2_minSS_TE(
design = "optimal", ## choose from "optimal", "minimax" and "unified"
unified.u = 1, ## specify when design = "unified", u in [0, 1]
nlooks = 1, ## number of interim looks (R)
skip_efficacy = NULL, ## A vector with a length equal to the number of stages. The default is NULL (no skip for efficacy). If skipping is enabled, set the corresponding stage to 1; otherwise, set it to 0. For example, c(1,0) indicates that futility monitoring is skipped in the first stage but applied in the second stage.
skip_toxicity = NULL, ## A vector with a length equal to the number of stages. The default is NULL (no skip for toxicity). If skipping is enabled, set the corresponding stage to 1; otherwise, set it to 0. For example, c(1,0) indicates that safety monitoring is skipped in the first stage but applied in the second stage.
maxPatients = 26, ## maximum number of patients
Nmin_cohort1 = 13, ## minimum number of first cohort
Nmin_increase = 13, ## minimum number of increase in each cohort
p01 = 0.3, ## efficacy under null
p02 = 0.4, ## toxicity under null
p03 = 0.2, ## efficacy and toxicity under null, quantifying the correlation between efficacy and toxicity
p11 = 0.6, ## efficacy under alternative
p12 = 0.2, ## toxicity under alternative
p13 = 0.15,## efficacy and toxicity under alternative, quantifying the correlation between efficacy and toxicity
err_eff = 0.1, ## Type I error for futile
err_tox = 0.1, ## Type I error for toxic
err_all = 0.05, ## Type I futile and toxic,
power_eff = 0.8, ## power for futile
power_tox = 0.8, ## power for toxic
power_all = 0.8, ## power for futile and toxic
pso_method = "all", ## choose from "all", ""default", "quantum" or "dexp"
nParallel = 3, ## number of PSO_ensemble
nSwarm = 32, ## nSwarm in PSO
maxIter = 100 ## maxIter in PSO
)
link = system.file("Flowchart", "progressbar.png", package = "GBOP2")
knitr::include_graphics(link)
init_cluster(5) ## Initialize 5 parallel cluster
Optimal_GO_TE <- GBOP2_minSS_TE(
design = "optimal", ## choose from "optimal", "minimax" and "unified"
unified.u = 1, ## specify when design = "unified", u in [0, 1]
nlooks = 1, ## number of interim looks (R)
skip_efficacy = NULL, ## A vector with a length equal to the number of stages. The default is NULL (no skip for efficacy). If skipping is enabled, set the corresponding stage to 1; otherwise, set it to 0. For example, c(1,0) indicates that futility monitoring is skipped in the first stage but applied in the second stage.
skip_toxicity = NULL, ## A vector with a length equal to the number of stages. The default is NULL (no skip for toxicity). If skipping is enabled, set the corresponding stage to 1; otherwise, set it to 0. For example, c(1,0) indicates that safety monitoring is skipped in the first stage but applied in the second stage.
maxPatients = 26, ## maximum number of patients
Nmin_cohort1 = 13, ## minimum number of first cohort
Nmin_increase = 13, ## minimum number of increase in each cohort
p01 = 0.3, ## efficacy under null
p02 = 0.4, ## toxicity under null
p03 = 0.2, ## efficacy and toxicity under null, quantifying the correlation between efficacy and toxicity
p11 = 0.6, ## efficacy under alternative
p12 = 0.2, ## toxicity under alternative
p13 = 0.15,## efficacy and toxicity under alternative, quantifying the correlation between efficacy and toxicity
err_eff = 0.1, ## Type I error for futile
err_tox = 0.1, ## Type I error for toxic
err_all = 0.05, ## Type I futile and toxic,
power_eff = 0.8, ## power for futile
power_tox = 0.8, ## power for toxic
power_all = 0.8, ## power for futile and toxic
pso_method = "all", ## choose from "all", ""default", "quantum" or "dexp"
nParallel = 3, ## number of PSO_ensemble
nSwarm = 32, ## nSwarm in PSO
maxIter = 100 ## maxIter in PSO
)
Interpreting
$H_{01}$ represents that the treatment is safe but futile, $H_{10}$ represents that the treatment is efficacious but toxic, and $H_{00}$ represents the global null that the treatment is futile and toxic. $H_{11}$ denote claiming that the treatment is efficacious and safe. The GBOP2_minSS_TE function identified an optimal two-stage design with interim times of 13 and 26 patients. The trial stops early for futility if efficacy responses are $≤$ 3 at stage 1 or $≤$ 10 at stage 2, and stops early for toxicity if $≥$ 5 toxicities occur at stage 1 or $≥$ 8 at stage 2. This design achieves over 80% power and efficiently balancing safety and effectiveness.
summary(Optimal_GO_TE)
Study Objective: power maximization
Objective function $L$:
link = system.file("Flowchart", "mp.png", package = "GBOP2")
knitr::include_graphics(link)
The parameters $u$ and $w$ are in the range $[0,1]$ and represent the pre-assigned weights for the study sample size $N$ and how it is distributed under the null hypothesis $H_0$ and the alternative hypothesis $H_1$, respectively. The score $g$ refers to some specified function on the sample size variable N and takes the unit-bounded value in $[0,1]$. The parameters $\alpha$ and $\beta$ refer to the Type I and Type II error. The constants $\alpha_0$ and $\beta_0$ refer to the nominal Type I and Type II error rates, while $n_{\text{max}}$ is the maximum allowable sample size.
Efficacy: single boundary for futility
The GBOP2_maxP_singleE function is designed to identify the design which maximizing the power for a single futility boundary, which meets the pre-specified Type I and Type II error rates by using PSO_GO algorithm. The parameters are the same as in the GBOP2_minSS_single function except for the totalPatients, which represents the total number of patients. Similarly, when users choose pso_method equals "all", GBOP2_maxP_single function conducts PSO_Ensemble in parallel by indicating the number of parallel through nParallel option. Otherwise, users can specify the pso_method option as default, quantum, or dexp to conduct a single PSO algorithm. The following example show the design maximizes the power identified by the PSO-GO.
## PSO-GO
init_cluster(5) ## Initialize 5 parallel cluster
power_GO_single <- GBOP2_maxP_singleE(
nlooks = 3, ## number of interim analyses
p0 = 0.2, ## response rate under null hypothesis
p1 = 0.4, ## response rate under alternative hypothesis
err1 = 0.05, ## Type I error rate
minPower = 0.8, ## desired minimum statistical power
totalPatients = 50, ## total number of patients
Nmin_cohort1 = 15, ## minimum cohort size for the first stage
Nmin_increase = 8, ## minimum increment in cohort size for subsequent stages
pso_method = "all", ## choose from "all", "default", "quantum" or "dexp"
nParallel = 3, ## number of parallel runs for global optimization
seed = 1024,
nSwarm = 64, ## nSwarm in PSO
maxIter = 200 ## maxIter in PSO
)
link = system.file("Flowchart", "progressbar.png", package = "GBOP2")
knitr::include_graphics(link)
## PSO-GO
init_cluster(5) ## Initialize 5 parallel cluster
power_GO_single <- GBOP2_maxP_singleE(
nlooks = 3, ## number of interim analyses
p0 = 0.2, ## response rate under null hypothesis
p1 = 0.4, ## response rate under alternative hypothesis
err1 = 0.05, ## Type I error rate
minPower = 0.8, ## desired minimum statistical power
totalPatients = 50, ## total number of patients
Nmin_cohort1 = 15, ## minimum cohort size for the first stage
Nmin_increase = 8, ## minimum increment in cohort size for subsequent stages
pso_method = "all", ## choose from "all", "default", "quantum" or "dexp"
nParallel = 3, ## number of parallel runs for global optimization
seed = 1024,
nSwarm = 64, ## nSwarm in PSO
maxIter = 200 ## maxIter in PSO
)
Interpretation
The GBOP2_maxP_singleE function successfully identified an optimal four-stage design for maximizing power under a single boundary setting. The design specifies interim analyses after recruiting 25, 34, 42, and 50 patients. The optimized stopping boundaries are set at 5, 8, 11, and 14 responses at each stage. Specifically, the trial will terminate early and declare the drug not promising if the number of observed responses at each stage is less than or equal to these respective boundaries. Otherwise, the trial proceeds to the next stage or concludes positively at the final stage. This design achieves a high statistical power
over 80% while maintaining a Type I error rate below 5%, balancing the ability to detect treatment efficacy with resource efficiency.
summary(power_GO_single)
Efficacy: dual boundary for futility monitoring and superiority detecting
The GBOP2_maxP_dual function is designed to identify the design that maximizing power for dual futility boundaries, which meets the pre-specified Type I and Type II error rates by using PSO_GO algorithm. When pso_method equals "all", GBOP2_maxP_dual function conducts PSO_Ensemble in parallel, otherwise, users can specify the pso_method option as default, quantum, or dexp to conduct a single PSO algorithm. The following example show the design that maximizing power for dual futility boundary identified by the PSO-GO.
## PSO-GO
init_cluster(5) ## Initialize 5 parallel cluster
power_GO_dual <- GBOP2_maxP_dualE(
design = "optimal", ## fixed as "optimal", which can not be modified by user
nlooks = 1, ## number of interim looks
p0 = 0.2, ## response rate under the null
p1 = 0.4, ## response rate under the alternative
err1 = 0.05, ## Type I error
minPower = 0.8, ## minimum desired power
totalPatients = 50, ## total number of patients
Nmin_cohort1 = 10, ## minimum cohort size at the first stage
Nmin_increase = 5, ## minimum increment of cohort size at subsequent stages
pso_method = "all", ## choose from "all", "default", "quantum" or "dexp"
nParallel = 3, ## number of parallel PSO_Ensemble
seed = 1024,
nSwarm = 64, ## nSwarm in PSO
maxIter = 400 ## maxIter in PSO
)
link = system.file("Flowchart", "progressbar.png", package = "GBOP2")
knitr::include_graphics(link)
## PSO-GO
init_cluster(5) ## Initialize 5 parallel cluster
power_GO_dual <- GBOP2_maxP_dualE(
design = "optimal", ## fixed as "optimal", which can not be modified by user
nlooks = 1, ## number of interim looks
p0 = 0.2, ## response rate under the null
p1 = 0.4, ## response rate under the alternative
err1 = 0.05, ## Type I error
minPower = 0.8, ## minimum desired power
totalPatients = 50, ## total number of patients
Nmin_cohort1 = 10, ## minimum cohort size at the first stage
Nmin_increase = 5, ## minimum increment of cohort size at subsequent stages
pso_method = "all", ## choose from "all", "default", "quantum" or "dexp"
nParallel = 3, ## number of parallel PSO_Ensemble
seed = 1024,
nSwarm = 64, ## nSwarm in PSO
maxIter = 400 ## maxIter in PSO
)
Interpretation
The GBOP2_maxP_dualE function identified an optimal two-stage design with interim times of 44 and 50 patients, maximizing power under dual boundaries. The trial will terminate early for futility if the number of responses is $≤$ 7 at stage 1 or $≤$ 15 at stage 2, or terminate early for efficacy if the number of responses is $≥$ 14 at stage 1 or $≥$ 16 at stage 2. This design achieves high statistical power (over 80%) while maintaining a controlled Type I error (less than 5%), effectively balancing trial efficiency, safety, and the ability to detect a true treatment effect.
summary(power_GO_dual)
Toxicity and Efficacy: joint safety and futility monitoring
The GBOP2_maxP_TE function is designed to identify the design that maximizing power for TE boundary, which meets the pre-specified Type I and Type II error rates by using PSO_GO algorithm. When pso_method equals "all", GBOP2_maxP_TE function conducts PSO_Ensemble in parallel, otherwise, users can specify the pso_method option as default, quantum, or dexp to conduct a single PSO algorithm. The following example show the design that maximizing power for TE boundary identified by the PSO-GO.
## PSO-GO
init_cluster(5) ## Initialize 5 parallel cluster
power_GO_TE <- GBOP2_maxP_TE(pso_method = "all", ## choose from "all", "default", "quantum" or "dexp"
nlooks =1 , ## number of interim analyses
skip_efficacy = NULL, ## Default: NULL no skipping for efficacy.
## Input a vector as the same length as total stages, 1 is skip.
## Ex: skip for efficacy for the first stage of a two stage design input c(1,0).
skip_toxicity = NULL, ## Default: NULL no skipping for toxicity.
## Input a vector as the same length as total stages, 1 is skip.
## Ex: skip for toxicity. for the first stage of a two stage design input c(1,0).
totalPatients = 26, ## total number of patients
Nmin_cohort1 = 10, ## minimum number of patients in the first stage
Nmin_increase = 5, ## minimum increment in cohort size per stage
p01 = 0.3, ## efficacy rate under null hypothesis
p02 = 0.4, ## toxicity rate under null hypothesis
p03 = 0.2, ## joint efficacy-toxicity correlation under null
p11 = 0.6, ## efficacy rate under alternative hypothesis
p12 = 0.2, ## toxicity rate under alternative hypothesis
p13 = 0.15, ## joint efficacy-toxicity correlation under alternative
err_eff = 0.1, ## Type I error for efficacy (futile but safe)
err_tox = 0.1, ## Type I error for toxicity (effective but toxic)
err_all = 0.05, ## Type I error for futile and toxic outcomes
power_eff = 0.8, ## power target for efficacy
power_tox = 0.8, ## power target for toxicity
power_all = 0.8, ## power target for joint efficacy-toxicity
nSwarm = 32, ## nSwarm in PSO
nParallel = 3, ## number of parallel PSO_Ensemble
seed = 123,
maxIter = 100 ## maxIter in PSO
)
link = system.file("Flowchart", "progressbar.png", package = "GBOP2")
knitr::include_graphics(link)
## PSO-GO
init_cluster(5) ## Initialize 5 parallel cluster
power_GO_TE <- GBOP2_maxP_TE(pso_method = "all",
nlooks =1 , ## number of interim analyses
skip_efficacy = NULL, ## Default: NULL no skipping for efficacy.
## Input a vector as the same length as total stages, 1 is skip.
## Ex: skip for efficacy for the first stage of a two stage design input c(1,0).
skip_toxicity = NULL, ## Default: NULL no skipping for toxicity.
## Input a vector as the same length as total stages, 1 is skip.
## Ex: skip for toxicity. for the first stage of a two stage design input c(1,0).
totalPatients = 26, ## total number of patients
Nmin_cohort1 = 10, ## minimum number of patients in the first stage
Nmin_increase = 5, ## minimum increment in cohort size per stage
p01 = 0.3, ## efficacy rate under null hypothesis
p02 = 0.4, ## toxicity rate under null hypothesis
p03 = 0.2, ## joint efficacy-toxicity correlation under null
p11 = 0.6, ## efficacy rate under alternative hypothesis
p12 = 0.2, ## toxicity rate under alternative hypothesis
p13 = 0.15, ## joint efficacy-toxicity correlation under alternative
err_eff = 0.1, ## Type I error for efficacy (futile but safe)
err_tox = 0.1, ## Type I error for toxicity (effective but toxic)
err_all = 0.05, ## Type I error for futile and toxic outcomes
power_eff = 0.8, ## power target for efficacy
power_tox = 0.8, ## power target for toxicity
power_all = 0.8, ## power target for joint efficacy-toxicity
nSwarm = 32, ## nSwarm in PSO
nParallel = 3, ## number of parallel PSO_Ensemble
seed = 123,
maxIter = 100 ## maxIter in PSO
)
Interpretation
The GBOP2_maxP_TE function identified an optimal two-stage design with interim times of 21 and 26 patients to maximize power under joint toxicity-efficacy (TE) boundaries. The trial stops early for futility (ineffectiveness) if efficacy responses are $≤$ 8 at stage 1 or $≤$ 9 at stage 2, and stops early for toxicity (safety concerns) if toxicities are $≥$ 7 at stage 1 or $≥$ 8 at stage 2. This design achieves a statistical power over 80%, effectively controlling Type I error rates and balancing patient safety, efficacy detection, and trial efficiency.
summary(power_GO_TE)
stop_cluster() ## Stops the currently running parallel cluster and reverts to sequential execution.
get_cluster() ## Parallel execution terminated — make sure returning NULL.
Reference
-
Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). ieee.
-
Sun, J., Xu, W., & Feng, B. (2004, December). A global search strategy of quantum-behaved particle swarm optimization. In IEEE Conference on Cybernetics and Intelligent Systems, 2004. (Vol. 1, pp. 111-116). IEEE.
-
Stehlík, M., Chen, P. Y., Wong, W. K., & Kiseľák, J. (2024). 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. Applied Soft Computing, 163, 111913.
-
Simon, R. (1989), ‘Optimal two-stage designs for phase ii clinical trials.’, Controlled clinical trials 10, 1–10.
-
Fleming, T. R. (1982). One-sample multiple testing procedure for phase II clinical trials. Biometrics, 143-151.
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GBOP2 documentation built on April 11, 2025, 5:42 p.m.
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) `%operator%` <- GBOP2:::`%operator%` # triple-colon to access non-exported object
Installation
The R package GBOP2 is available for free on the Comprehensive R Archive Network (CRAN). It offers functions for implementing G-BOP2 design with various Particle Swarm Optimization (PSO) algorithms (Kennedy \& Eberhart 1995), including PSO-Default (Kennedy \& Eberhart, 1995), PSO-Quantum (Sun, Xu \& Feng 2004), PSO-DEXP (Stehl´ık et al. 2024), and PSO-GO. The G-BOP2 design includes two objectives, one is the sample size minimization, and the other is the power maximization. Parallel computing is only used when the user explicitly sets nCore > 1. The function defaults to sequential execution. If you plan to run parallel computing, you need to manually controlling the parallel backend using init_cluster(nCore) and stop_cluster(). The parallel backend will be automatically stopped when a function completes, so you will need to set nCore again each time before running the function. You can load the package using the following command:
#install.packages("GBOP2") library(GBOP2)
Summary of functions
library(knitr) # Create the data frame data <- data.frame( Category = c("Sample Size Minimization", "Power Maximization", ""), `Efficacy - Single Boundary` = c("GBOP2_minSS_singleE", "GBOP2_maxP_singleE", ""), `Efficacy - Dual Boundary` = c("GBOP2_minSS_dualE", "GBOP2_maxP_dualE", ""), `Toxicity and Efficacy` = c("GBOP2_minSS_TE", "GBOP2_maxP_TE", "") ) # Render the table kable(data, align = "c", col.names = c("", "Efficacy - Single Boundary", "Efficacy - Dual Boundary", "Toxicity and Efficacy"))
Study Objective: sample size minimization
Efficacy: single boundary for futility
Objective function $L$:
link = system.file("Flowchart", "mss.png", package = "GBOP2") knitr::include_graphics(link)
The parameters $u$ and $w$ are in the range $[0,1]$ and represent the pre-assigned weights for the study sample size $N$ and how it is distributed under the null hypothesis $H_0$ and the alternative hypothesis $H_1$, respectively. The score $g$ refers to some specified function on the sample size variable N and takes the unit-bounded value in $[0,1]$. The parameters $\alpha$ and $\beta$ refer to the Type I and Type II error. The constants $\alpha_0$ and $\beta_0$ refer to the nominal Type I and Type II error rates, while $n_{\text{max}}$ is the maximum allowable sample size. While $w$ is often set to 0, 1, or 1/2 in optimal designs to indicate diverse trial-specific considerations on $H_0$ and $H_1$, we recommend $w= 2/3$ to reflect the observation that only 1/3 of phase II studies successfully transit to phase III, suggesting empirically 2/3 chance in favor of $H_0$.
For example:
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Simon's two stage optimal design (Simon 1989): $$ g(N \mid H_.) = E(N \mid H_.)/n, u = 0, w = 1 $$
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Simon's two stage minimax design (Simon 1989): $$ g(N \mid H_.) = E(N \mid H_.)/n, u = 1, w =1 $$
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Fleming 1982 $$ g(N \mid H_.) = E(N \mid H_.)/n, u = 0, w = 1 $$
The following examples illustrate the different designs identified by the PSO-GO algorithm. Note that the PSO-GO algorithm guarantees convergence to the global optimum almost surely when using a large nParallel value. If the global optimum is not reached, try increasing the nParallel value or running the algorithm again with different seeds.
The GBOP2_minSS_single function is designed to identify the optimal G-BOP2 design for single futility boundary. The optimal G-BOP2 design aims to minimize the sample size target specified in the objective function among the designs that satisfy the pre-specified nominal Type I error and minimum statistical power. Parameters such as nlooks, weight, maxPatients, Nmin_cohort1, and Nmin_increase provide users with more flexibility to adjust the number of interim looks, the weight of the sample size under the null hypothesis, and other design characteristics. When users choose pso_method equals "all", GBOP2_minSS_single function conducts PSO_Ensemble in parallel by indicating the number of parallel through nParallel option. Otherwise, users can specify the pso_method option as default, quantum, or dexp to conduct a single PSO algorithm. The following example show the optimal 3-stage G-BOP2 optimal design identified by the PSO-GO with $u=1$, $w=1$.
## PSO-GO init_cluster(5) ## Initialize 5 parallel cluster optimal_GO_single <- GBOP2_minSS_singleE( design = "optimal", ## "optimal" or "minimax", "unified" unified.u = 1, ## specify when design = "unified", u in [0, 1] weight = 1, ## weight of sample size under null, w in [0, 1] nlooks = 2, ## number of interim looks. For 3-stage design, nlooks = 2. p0 = 0.2, ## response rate in null hypothesis p1 = 0.4, ## response rate in alternative hypothesis err1 = 0.05, ## Type Ierror minPower = 0.8, ## power maxPatients = 50, ## maximum number of patients Nmin_cohort1 = 5, ## minimum number of first cohort Nmin_increase = 5, ## minimum number of increase in each cohort pso_method = "all", ## choose from "all", "default", "quantum" or "dexp" nParallel = 3, ## number of PSO-ensemble, only effective when pso_method = "all" seed = 456, nSwarm = 64, ## nSwarm in PSO maxIter = 200) ## maxIter in PSO
link = system.file("Flowchart", "progressbar.png", package = "GBOP2") knitr::include_graphics(link)
## PSO-GO init_cluster(5) ## Initialize 5 parallel cluster optimal_GO_single <- GBOP2_minSS_singleE( design = "optimal", ## "optimal" or "minimax", "unified" unified.u = 1, ## specify when design = "unified", u in [0, 1] weight = 1, ## weight of sample size under null, w in [0, 1] nlooks = 2, ## number of interim looks. For 3-stage design, nlooks = 2. p0 = 0.2, ## response rate in null hypothesis p1 = 0.4, ## response rate in alternative hypothesis err1 = 0.05, ## Type Ierror minPower = 0.8, ## power maxPatients = 50, ## maximum number of patients Nmin_cohort1 = 5, ## minimum number of first cohort Nmin_increase = 5, ## minimum number of increase in each cohort pso_method = "all", ## choose from "all", "default", "quantum" or "dexp" nParallel = 3, ## number of PSO-ensemble, only effective when pso_method = "all" seed = 456, nSwarm = 64, ## nSwarm in PSO maxIter = 200) ## maxIter in PSO
Interpretation:
The GBOP2_minSS_singleE function successfully identified an optimal three-stage design with a power of over 80% and a Type I error rate of less than 5% . The optimized interim times are 8, 22, and 38 respectively, and the optimized stopping boundaries for each stage are 1, 5, and 11. The trial will be terminated and the drug will be considered not promising if the observed number of responses at any stage is less than or equal to the corresponding stopping boundary. Otherwise, the drug will be declared promising. The parameters section provides key values used in determining the optimal boundaries.
summary(optimal_GO_single)
Efficacy: dual boundary for futility monitoring and superiority detecting
The GBOP2_minSS_dual function is designed to identify the optimal and minimax designs for dual futility boundaries, which meets the pre-specified Type I and Type II error rates by using PSO_GO algorithm. When pso_method equals "all", GBOP2_minSS_dual function conducts PSO_Ensemble in parallel, otherwise, users can specify the pso_method option as default, quantum, or dexp to conduct a single PSO algorithm. The following example show the optimal design for dual futility boundary identified by the PSO-GO.
## PSO-GO init_cluster(5) ## Initialize 5 parallel cluster optimal_GO_dual <- GBOP2_minSS_dualE( design = "optimal", ## choose from "optimal", "minimax" and "unified" unified.u = 1, ## specify when design = "unified", u in [0, 1] weight = 0.5, ## weight of sample size under null nlooks = 1, ## number of interim looks (R) p0 = 0.2, ## response rate in null hypothesis p1 = 0.4, ## response rate in alternative hypothesis err1 = 0.05, ## Type I error minPower = 0.8, ## power maxPatients = 50, ## maximum number of patients Nmin_cohort1 = 10, ## minimum number of first cohort Nmin_increase = 5, ## minimum number of increase in each cohort pso_method = "all", ## choose from "all", ""default", "quantum" or "dexp" nParallel = 3, ## number of PSO_ensemble seed = 123, nSwarm = 64, ## nSwarm in PSO maxIter = 400) ## maxIter in PSO
link = system.file("Flowchart", "progressbar.png", package = "GBOP2") knitr::include_graphics(link)
## PSO-GO init_cluster(5) ## Initialize 5 parallel cluster optimal_GO_dual <- GBOP2_minSS_dualE( design = "optimal", ## choose from "optimal", "minimax" and "unified" unified.u = 1, ## specify when design = "unified", u in [0, 1] weight = 0.5, ## weight of sample size under null nlooks = 1, ## number of interim looks (R) p0 = 0.2, ## response rate in null hypothesis p1 = 0.4, ## response rate in alternative hypothesis err1 = 0.05, ## Type I error minPower = 0.8, ## power maxPatients = 50, ## maximum number of patients Nmin_cohort1 = 10, ## minimum number of first cohort Nmin_increase = 5, ## minimum number of increase in each cohort pso_method = "all", ## choose from "all", ""default", "quantum" or "dexp" nParallel = 3, ## number of PSO_ensemble seed = 123, nSwarm = 64, ## nSwarm in PSO maxIter = 400 ## maxIter in PSO )
Interpretation:
The GBOP2_minSS_dualE function successfully identified an optimal two-stage design with a power exceeding 80% and a Type I error rate below 5%. The optimized interim times are 14 and 37, respectively. The stopping boundaries for futility are 3 and 11, and the boundaries for efficacy are 7 and 12.The trial will be terminated for futility, and the drug will be considered not promising if the observed number of responses is $≤$ 3 in the first stage or $≤$ 11 in the second stage. Conversely, the trial will be terminated for efficacy, and the drug will be considered promising if the observed number of responses is $≥$ 7 in the first stage or $≥$ 12 in the second stage.The parameters section provides key values used in determining the optimal boundaries.
summary(optimal_GO_dual)
Toxicity and Efficacy: joint safety and futility monitoring
The GBOP2_minSS_TE function is designed to identify the optimal and minimax designs for toxicity and efficacy boundaries, which meets the pre-specified Type I and Type II error rates by using PSO_GO algorithm. When pso_method equals "all", GBOP2_minSS_dual function conducts PSO_Ensemble in parallel, otherwise, users can specify the pso_method option as default, quantum, or dexp to conduct a single PSO algorithm. The following example show the optimal design for dual futility boundaries identified by the PSO-GO.
init_cluster(5) ## Initialize 5 parallel cluster Optimal_GO_TE <- GBOP2_minSS_TE( design = "optimal", ## choose from "optimal", "minimax" and "unified" unified.u = 1, ## specify when design = "unified", u in [0, 1] nlooks = 1, ## number of interim looks (R) skip_efficacy = NULL, ## A vector with a length equal to the number of stages. The default is NULL (no skip for efficacy). If skipping is enabled, set the corresponding stage to 1; otherwise, set it to 0. For example, c(1,0) indicates that futility monitoring is skipped in the first stage but applied in the second stage. skip_toxicity = NULL, ## A vector with a length equal to the number of stages. The default is NULL (no skip for toxicity). If skipping is enabled, set the corresponding stage to 1; otherwise, set it to 0. For example, c(1,0) indicates that safety monitoring is skipped in the first stage but applied in the second stage. maxPatients = 26, ## maximum number of patients Nmin_cohort1 = 13, ## minimum number of first cohort Nmin_increase = 13, ## minimum number of increase in each cohort p01 = 0.3, ## efficacy under null p02 = 0.4, ## toxicity under null p03 = 0.2, ## efficacy and toxicity under null, quantifying the correlation between efficacy and toxicity p11 = 0.6, ## efficacy under alternative p12 = 0.2, ## toxicity under alternative p13 = 0.15,## efficacy and toxicity under alternative, quantifying the correlation between efficacy and toxicity err_eff = 0.1, ## Type I error for futile err_tox = 0.1, ## Type I error for toxic err_all = 0.05, ## Type I futile and toxic, power_eff = 0.8, ## power for futile power_tox = 0.8, ## power for toxic power_all = 0.8, ## power for futile and toxic pso_method = "all", ## choose from "all", ""default", "quantum" or "dexp" nParallel = 3, ## number of PSO_ensemble nSwarm = 32, ## nSwarm in PSO maxIter = 100 ## maxIter in PSO )
link = system.file("Flowchart", "progressbar.png", package = "GBOP2") knitr::include_graphics(link)
init_cluster(5) ## Initialize 5 parallel cluster Optimal_GO_TE <- GBOP2_minSS_TE( design = "optimal", ## choose from "optimal", "minimax" and "unified" unified.u = 1, ## specify when design = "unified", u in [0, 1] nlooks = 1, ## number of interim looks (R) skip_efficacy = NULL, ## A vector with a length equal to the number of stages. The default is NULL (no skip for efficacy). If skipping is enabled, set the corresponding stage to 1; otherwise, set it to 0. For example, c(1,0) indicates that futility monitoring is skipped in the first stage but applied in the second stage. skip_toxicity = NULL, ## A vector with a length equal to the number of stages. The default is NULL (no skip for toxicity). If skipping is enabled, set the corresponding stage to 1; otherwise, set it to 0. For example, c(1,0) indicates that safety monitoring is skipped in the first stage but applied in the second stage. maxPatients = 26, ## maximum number of patients Nmin_cohort1 = 13, ## minimum number of first cohort Nmin_increase = 13, ## minimum number of increase in each cohort p01 = 0.3, ## efficacy under null p02 = 0.4, ## toxicity under null p03 = 0.2, ## efficacy and toxicity under null, quantifying the correlation between efficacy and toxicity p11 = 0.6, ## efficacy under alternative p12 = 0.2, ## toxicity under alternative p13 = 0.15,## efficacy and toxicity under alternative, quantifying the correlation between efficacy and toxicity err_eff = 0.1, ## Type I error for futile err_tox = 0.1, ## Type I error for toxic err_all = 0.05, ## Type I futile and toxic, power_eff = 0.8, ## power for futile power_tox = 0.8, ## power for toxic power_all = 0.8, ## power for futile and toxic pso_method = "all", ## choose from "all", ""default", "quantum" or "dexp" nParallel = 3, ## number of PSO_ensemble nSwarm = 32, ## nSwarm in PSO maxIter = 100 ## maxIter in PSO )
Interpreting
$H_{01}$ represents that the treatment is safe but futile, $H_{10}$ represents that the treatment is efficacious but toxic, and $H_{00}$ represents the global null that the treatment is futile and toxic. $H_{11}$ denote claiming that the treatment is efficacious and safe. The GBOP2_minSS_TE function identified an optimal two-stage design with interim times of 13 and 26 patients. The trial stops early for futility if efficacy responses are $≤$ 3 at stage 1 or $≤$ 10 at stage 2, and stops early for toxicity if $≥$ 5 toxicities occur at stage 1 or $≥$ 8 at stage 2. This design achieves over 80% power and efficiently balancing safety and effectiveness.
summary(Optimal_GO_TE)
Study Objective: power maximization
Objective function $L$:
link = system.file("Flowchart", "mp.png", package = "GBOP2") knitr::include_graphics(link)
The parameters $u$ and $w$ are in the range $[0,1]$ and represent the pre-assigned weights for the study sample size $N$ and how it is distributed under the null hypothesis $H_0$ and the alternative hypothesis $H_1$, respectively. The score $g$ refers to some specified function on the sample size variable N and takes the unit-bounded value in $[0,1]$. The parameters $\alpha$ and $\beta$ refer to the Type I and Type II error. The constants $\alpha_0$ and $\beta_0$ refer to the nominal Type I and Type II error rates, while $n_{\text{max}}$ is the maximum allowable sample size.
Efficacy: single boundary for futility
The GBOP2_maxP_singleE function is designed to identify the design which maximizing the power for a single futility boundary, which meets the pre-specified Type I and Type II error rates by using PSO_GO algorithm. The parameters are the same as in the GBOP2_minSS_single function except for the totalPatients, which represents the total number of patients. Similarly, when users choose pso_method equals "all", GBOP2_maxP_single function conducts PSO_Ensemble in parallel by indicating the number of parallel through nParallel option. Otherwise, users can specify the pso_method option as default, quantum, or dexp to conduct a single PSO algorithm. The following example show the design maximizes the power identified by the PSO-GO.
## PSO-GO init_cluster(5) ## Initialize 5 parallel cluster power_GO_single <- GBOP2_maxP_singleE( nlooks = 3, ## number of interim analyses p0 = 0.2, ## response rate under null hypothesis p1 = 0.4, ## response rate under alternative hypothesis err1 = 0.05, ## Type I error rate minPower = 0.8, ## desired minimum statistical power totalPatients = 50, ## total number of patients Nmin_cohort1 = 15, ## minimum cohort size for the first stage Nmin_increase = 8, ## minimum increment in cohort size for subsequent stages pso_method = "all", ## choose from "all", "default", "quantum" or "dexp" nParallel = 3, ## number of parallel runs for global optimization seed = 1024, nSwarm = 64, ## nSwarm in PSO maxIter = 200 ## maxIter in PSO )
link = system.file("Flowchart", "progressbar.png", package = "GBOP2") knitr::include_graphics(link)
## PSO-GO init_cluster(5) ## Initialize 5 parallel cluster power_GO_single <- GBOP2_maxP_singleE( nlooks = 3, ## number of interim analyses p0 = 0.2, ## response rate under null hypothesis p1 = 0.4, ## response rate under alternative hypothesis err1 = 0.05, ## Type I error rate minPower = 0.8, ## desired minimum statistical power totalPatients = 50, ## total number of patients Nmin_cohort1 = 15, ## minimum cohort size for the first stage Nmin_increase = 8, ## minimum increment in cohort size for subsequent stages pso_method = "all", ## choose from "all", "default", "quantum" or "dexp" nParallel = 3, ## number of parallel runs for global optimization seed = 1024, nSwarm = 64, ## nSwarm in PSO maxIter = 200 ## maxIter in PSO )
Interpretation
The GBOP2_maxP_singleE function successfully identified an optimal four-stage design for maximizing power under a single boundary setting. The design specifies interim analyses after recruiting 25, 34, 42, and 50 patients. The optimized stopping boundaries are set at 5, 8, 11, and 14 responses at each stage. Specifically, the trial will terminate early and declare the drug not promising if the number of observed responses at each stage is less than or equal to these respective boundaries. Otherwise, the trial proceeds to the next stage or concludes positively at the final stage. This design achieves a high statistical power
over 80% while maintaining a Type I error rate below 5%, balancing the ability to detect treatment efficacy with resource efficiency.
summary(power_GO_single)
Efficacy: dual boundary for futility monitoring and superiority detecting
The GBOP2_maxP_dual function is designed to identify the design that maximizing power for dual futility boundaries, which meets the pre-specified Type I and Type II error rates by using PSO_GO algorithm. When pso_method equals "all", GBOP2_maxP_dual function conducts PSO_Ensemble in parallel, otherwise, users can specify the pso_method option as default, quantum, or dexp to conduct a single PSO algorithm. The following example show the design that maximizing power for dual futility boundary identified by the PSO-GO.
## PSO-GO init_cluster(5) ## Initialize 5 parallel cluster power_GO_dual <- GBOP2_maxP_dualE( design = "optimal", ## fixed as "optimal", which can not be modified by user nlooks = 1, ## number of interim looks p0 = 0.2, ## response rate under the null p1 = 0.4, ## response rate under the alternative err1 = 0.05, ## Type I error minPower = 0.8, ## minimum desired power totalPatients = 50, ## total number of patients Nmin_cohort1 = 10, ## minimum cohort size at the first stage Nmin_increase = 5, ## minimum increment of cohort size at subsequent stages pso_method = "all", ## choose from "all", "default", "quantum" or "dexp" nParallel = 3, ## number of parallel PSO_Ensemble seed = 1024, nSwarm = 64, ## nSwarm in PSO maxIter = 400 ## maxIter in PSO )
link = system.file("Flowchart", "progressbar.png", package = "GBOP2") knitr::include_graphics(link)
## PSO-GO init_cluster(5) ## Initialize 5 parallel cluster power_GO_dual <- GBOP2_maxP_dualE( design = "optimal", ## fixed as "optimal", which can not be modified by user nlooks = 1, ## number of interim looks p0 = 0.2, ## response rate under the null p1 = 0.4, ## response rate under the alternative err1 = 0.05, ## Type I error minPower = 0.8, ## minimum desired power totalPatients = 50, ## total number of patients Nmin_cohort1 = 10, ## minimum cohort size at the first stage Nmin_increase = 5, ## minimum increment of cohort size at subsequent stages pso_method = "all", ## choose from "all", "default", "quantum" or "dexp" nParallel = 3, ## number of parallel PSO_Ensemble seed = 1024, nSwarm = 64, ## nSwarm in PSO maxIter = 400 ## maxIter in PSO )
Interpretation
The GBOP2_maxP_dualE function identified an optimal two-stage design with interim times of 44 and 50 patients, maximizing power under dual boundaries. The trial will terminate early for futility if the number of responses is $≤$ 7 at stage 1 or $≤$ 15 at stage 2, or terminate early for efficacy if the number of responses is $≥$ 14 at stage 1 or $≥$ 16 at stage 2. This design achieves high statistical power (over 80%) while maintaining a controlled Type I error (less than 5%), effectively balancing trial efficiency, safety, and the ability to detect a true treatment effect.
summary(power_GO_dual)
Toxicity and Efficacy: joint safety and futility monitoring
The GBOP2_maxP_TE function is designed to identify the design that maximizing power for TE boundary, which meets the pre-specified Type I and Type II error rates by using PSO_GO algorithm. When pso_method equals "all", GBOP2_maxP_TE function conducts PSO_Ensemble in parallel, otherwise, users can specify the pso_method option as default, quantum, or dexp to conduct a single PSO algorithm. The following example show the design that maximizing power for TE boundary identified by the PSO-GO.
## PSO-GO init_cluster(5) ## Initialize 5 parallel cluster power_GO_TE <- GBOP2_maxP_TE(pso_method = "all", ## choose from "all", "default", "quantum" or "dexp" nlooks =1 , ## number of interim analyses skip_efficacy = NULL, ## Default: NULL no skipping for efficacy. ## Input a vector as the same length as total stages, 1 is skip. ## Ex: skip for efficacy for the first stage of a two stage design input c(1,0). skip_toxicity = NULL, ## Default: NULL no skipping for toxicity. ## Input a vector as the same length as total stages, 1 is skip. ## Ex: skip for toxicity. for the first stage of a two stage design input c(1,0). totalPatients = 26, ## total number of patients Nmin_cohort1 = 10, ## minimum number of patients in the first stage Nmin_increase = 5, ## minimum increment in cohort size per stage p01 = 0.3, ## efficacy rate under null hypothesis p02 = 0.4, ## toxicity rate under null hypothesis p03 = 0.2, ## joint efficacy-toxicity correlation under null p11 = 0.6, ## efficacy rate under alternative hypothesis p12 = 0.2, ## toxicity rate under alternative hypothesis p13 = 0.15, ## joint efficacy-toxicity correlation under alternative err_eff = 0.1, ## Type I error for efficacy (futile but safe) err_tox = 0.1, ## Type I error for toxicity (effective but toxic) err_all = 0.05, ## Type I error for futile and toxic outcomes power_eff = 0.8, ## power target for efficacy power_tox = 0.8, ## power target for toxicity power_all = 0.8, ## power target for joint efficacy-toxicity nSwarm = 32, ## nSwarm in PSO nParallel = 3, ## number of parallel PSO_Ensemble seed = 123, maxIter = 100 ## maxIter in PSO )
link = system.file("Flowchart", "progressbar.png", package = "GBOP2") knitr::include_graphics(link)
## PSO-GO init_cluster(5) ## Initialize 5 parallel cluster power_GO_TE <- GBOP2_maxP_TE(pso_method = "all", nlooks =1 , ## number of interim analyses skip_efficacy = NULL, ## Default: NULL no skipping for efficacy. ## Input a vector as the same length as total stages, 1 is skip. ## Ex: skip for efficacy for the first stage of a two stage design input c(1,0). skip_toxicity = NULL, ## Default: NULL no skipping for toxicity. ## Input a vector as the same length as total stages, 1 is skip. ## Ex: skip for toxicity. for the first stage of a two stage design input c(1,0). totalPatients = 26, ## total number of patients Nmin_cohort1 = 10, ## minimum number of patients in the first stage Nmin_increase = 5, ## minimum increment in cohort size per stage p01 = 0.3, ## efficacy rate under null hypothesis p02 = 0.4, ## toxicity rate under null hypothesis p03 = 0.2, ## joint efficacy-toxicity correlation under null p11 = 0.6, ## efficacy rate under alternative hypothesis p12 = 0.2, ## toxicity rate under alternative hypothesis p13 = 0.15, ## joint efficacy-toxicity correlation under alternative err_eff = 0.1, ## Type I error for efficacy (futile but safe) err_tox = 0.1, ## Type I error for toxicity (effective but toxic) err_all = 0.05, ## Type I error for futile and toxic outcomes power_eff = 0.8, ## power target for efficacy power_tox = 0.8, ## power target for toxicity power_all = 0.8, ## power target for joint efficacy-toxicity nSwarm = 32, ## nSwarm in PSO nParallel = 3, ## number of parallel PSO_Ensemble seed = 123, maxIter = 100 ## maxIter in PSO )
Interpretation
The GBOP2_maxP_TE function identified an optimal two-stage design with interim times of 21 and 26 patients to maximize power under joint toxicity-efficacy (TE) boundaries. The trial stops early for futility (ineffectiveness) if efficacy responses are $≤$ 8 at stage 1 or $≤$ 9 at stage 2, and stops early for toxicity (safety concerns) if toxicities are $≥$ 7 at stage 1 or $≥$ 8 at stage 2. This design achieves a statistical power over 80%, effectively controlling Type I error rates and balancing patient safety, efficacy detection, and trial efficiency.
summary(power_GO_TE)
stop_cluster() ## Stops the currently running parallel cluster and reverts to sequential execution. get_cluster() ## Parallel execution terminated — make sure returning NULL.
Reference
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Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). ieee.
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Sun, J., Xu, W., & Feng, B. (2004, December). A global search strategy of quantum-behaved particle swarm optimization. In IEEE Conference on Cybernetics and Intelligent Systems, 2004. (Vol. 1, pp. 111-116). IEEE.
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Stehlík, M., Chen, P. Y., Wong, W. K., & Kiseľák, J. (2024). 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. Applied Soft Computing, 163, 111913.
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Simon, R. (1989), ‘Optimal two-stage designs for phase ii clinical trials.’, Controlled clinical trials 10, 1–10.
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Fleming, T. R. (1982). One-sample multiple testing procedure for phase II clinical trials. Biometrics, 143-151.
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