Package 'bfboinet' reference manual

Title:	Backfill Bayesian Optimal Interval Design Using Efficacy and Toxicity
Description:	The backfill Bayesian optimal interval design using efficacy and toxicity outcomes for dose optimization (BF-BOIN-ET) design is a novel clinical trial design to allow patients to be backfilled at lower doses during a dose-finding trial while prioritizing the dose-escalation cohort to explore a higher dose. The advantages compared to the other designs in terms of the percentage of correct optimal dose (OD) selection, reducing the sample size, and shortening the duration of the trial, in various realistic setting.
Authors:	Jing Zhu [cre, aut], Kentaro Takeda [aut]
Maintainer:	Jing Zhu <[email protected]>
License:	GPL-3
Version:	0.2.0
Built:	2025-03-12 05:47:12 UTC
Source:	https://github.com/cran/bfboinet

backboinet

Description

Obtain the operating characteristics of the backfill bayesian optimal interval design using efficacy and toxicity outcomes for dose optimization within fixed scenarios

Usage

get.oc.backboinet(
  target_T = 0.3,
  toxprob,
  target_E = 0.25,
  effprob,
  n.dose,
  startdose,
  ncohort,
  cohortsize,
  pT.saf = 0.6 * target_T,
  pT.tox = 1.4 * target_T,
  pE.saf = 0.6 * target_E,
  alpha.T1 = 0.5,
  alpha.E1 = 0.5,
  tau.T,
  tau.E,
  te.corr = 0.2,
  gen.event.time = "weibull",
  accrual,
  gen.enroll.time = "uniform",
  n.earlystop = 6,
  stopping.npts = 12,
  suspend = 0,
  stopping.prob.T = 0.95,
  stopping.prob.E = 0.9,
  ppsi01 = 0,
  ppsi00 = 40,
  ppsi11 = 60,
  ppsi10 = 100,
  n.sim = 1000,
  seed.sim = 100
)
get.oc.backboinet(
  target_T = 0.3,
  toxprob,
  target_E = 0.25,
  effprob,
  n.dose,
  startdose,
  ncohort,
  cohortsize,
  pT.saf = 0.6 * target_T,
  pT.tox = 1.4 * target_T,
  pE.saf = 0.6 * target_E,
  alpha.T1 = 0.5,
  alpha.E1 = 0.5,
  tau.T,
  tau.E,
  te.corr = 0.2,
  gen.event.time = "weibull",
  accrual,
  gen.enroll.time = "uniform",
  n.earlystop = 6,
  stopping.npts = 12,
  suspend = 0,
  stopping.prob.T = 0.95,
  stopping.prob.E = 0.9,
  ppsi01 = 0,
  ppsi00 = 40,
  ppsi11 = 60,
  ppsi10 = 100,
  n.sim = 1000,
  seed.sim = 100
)

Arguments

`target_T`	Target toxicity probability. The default value is `target_T=0.3`. When observing 1 DLT out of 3 patients and the target DLT rate is between 0.25 and 0.279, the decision is to stay at the current dose due to a widely accepted practice.
`toxprob`	Vector of true toxicity probability.
`target_E`	The minimum required efficacy probability. The default value is `target_E=0.25`.
`effprob`	Vector of true efficacy probability.
`n.dose`	Number of dose.
`startdose`	Starting dose. The lowest dose is generally recommended.
`ncohort`	Number of cohort.
`cohortsize`	Cohort size.
`pT.saf`	Highest toxicity probability that is deemed sub-therapeutic such that dose-escalation should be pursued. The default value is `pT.saf=target_T*0.6`.
`pT.tox`	Lowest toxicity probability that is deemed overly toxic such that dose de-escalation is needed. The default value is `pT.tox=target_T*1.4`.
`pE.saf`	Minimum probability deemed efficacious such that the dose levels with less than delta1 are considered sub-therapeutic. The default value is `pE.saf=target_E*0.6`.
`alpha.T1`	Probability that toxicity event occurs in the late half of toxicity assessment window. The default value is `alpha.T1=0.5`.
`alpha.E1`	Probability that efficacy event occurs in the late half of assessment window. The default value is `alpha.E1=0.5`.
`tau.T`	Toxicity assessment windows (months).
`tau.E`	Efficacy assessment windows (months).
`te.corr`	Correlation between toxicity and efficacy probability, specified as Gaussian copula parameter. The default value is `te.corr=0.2`.
`gen.event.time`	Method to generate the time to first toxicity and efficacy outcome. Weibull distribution is used when `gen.event.time ="weibull"`. Uniform distribution is used when `gen.event.time="uniform"`. The default value is `gen.event.time="weibull"`.
`accrual`	Accrual rate (months) (patient accrual rate per month).
`gen.enroll.time`	Method to generate enrollment time. Uniform distribution is used when `gen.enroll.time="uniform"`. Exponential distribution is used when `gen.enroll.time="exponential"`. The default value is `gen.enroll.time="uniform"`.
`n.earlystop`	the early stopping parameter. If the number of patients treated at the current dose reaches `n.earlystop`,stop the trial and select the optimal dose (OD) based on the observed data.The default value is `n.earlystop=6`.
`stopping.npts`	Early study termination criteria for the number of patients in the dose-escalation and backfill cohorts. If the number of patients at the current dose reaches this criteria and the same dose level is recommended as the next dose level, the study is terminated. The default value is `stopping.npts=12`.
`suspend`	The suspension rule that holds off the decision on dose allocation for the dose-escalation cohort until sufficient toxicity information is available. For example, setting as 0.33 which means one-third of the patients had not completed the toxicity evaluation at the current dose level in the dose escalation cohort. The default value `suspend=0` essentially turns off this type of suspending rule, that is all patients should complete the toxicity evaluation at the current dose level in the dose escalation cohort
`stopping.prob.T`	Early study termination criteria for toxicity, taking a value between 0 and 1. If the posterior probability that toxicity outcome is less than the target toxicity probability (`target_T`) is larger than this criteria, the dose levels are eliminated from the study. The default value is `stopping.prob.T=0.95`.
`stopping.prob.E`	Early study termination criteria for efficacy, taking a value between 0 and 1. If the posterior probability that efficacy outcome is less than the minimum efficacy probability (`target_E`) is larger than this criteria, the dose levels are eliminated from the study. The default value is `stopping.prob.E=0.90`.
`ppsi01`	Score for toxicity=yes and efficacy=no in utility defined by scoring.The default value is `psi01=0`.
`ppsi00`	Score for toxicity=no and efficacy=no in utility defined by scoring. The default value is `psi00=40`.
`ppsi11`	Score for toxicity=yes and efficacy=yes in utility defined by scoring. The default value is `psi11=60`.
`ppsi10`	Score for toxicity=no and efficacy=yes in utility defined by scoring. The default value is `psi10=100`.
`n.sim`	Number of simulated trial. The default value is `n.sim=1000`.
`seed.sim`	Seed for random number generator. The default value is `seed.sim=100`.

Details

The backboinet is a function which generates the operating characteristics of the backfill bayesian optimal interval design using efficacy and toxicity outcomes for dose optimization by a simulation study. Users can specify a variety of study settings to simulate studies. The operating characteristics of the design are summarized by the percentage of times that each dose level was selected as optimal biological dose and the average number of patients who were treated at each dose level. The percentage of times that the study was terminated and the expected study duration are also provided.

Value

The backboinet returns a list containing the following components:

`toxprob`	True toxicity probability.
`effprob`	True efficacy probability.
`phi`	Target toxicity probability.
`delta`	Target efficacy probability.
`lambda1`	Lower toxicity boundary in dose escalation/de-escalation.
`lambda2`	Upper toxicity boundary in dose escalation/de-escalation.
`eta1`	Lower efficacy boundary in dose escalation/de-escalation.
`tau.T`	Toxicity assessment windows (months).
`tau.E`	Efficacy assessment windows (months).
`suspend`	The suspension rule that holds off the decision on dose allocation for the dose-escalation cohort until sufficient toxicity information is available.
`accrual`	Accrual rate (months) (patient accrual rate per month).
`n.patient`	Average number of patients who were treated at each dose level
`n.bpatient`	Average number of back filled patients who were treated at each dose level
`prop.select`	Percentage of times that each dose level was selected as optimal biological dose.
`prop.stop`	Percentage of times that the study was terminated.
`duration`	Expected study duration (months)
`totaln`	Total patients
`data.obs.n`	Record the number of patients in each dose level within the simulations during the trial
`obd`	Record the optimal dose in each simulation during the trial
`backfilltimes`	Record how may times we back-filled during the trial
`backfillcount`	Record the number of back-filled patients in dose level within the simulations during the trial
`PCS`	The percentage of trials that the optimal dose was correctly selected.
`PCA`	The percentage of patients that were correctly allocated to the optimal dose.
`PTS`	The percentage of toxic doses selection.
`PTA`	The percentage of patients who were allocated to toxic doses.

References

BF-BOIN-ET: A backfill Bayesian optimal interval design using efficacy and toxicity outcomes for dose optimization.

Examples


target_T=0.3
target_E=0.25
toxprob=c(0.03,0.05,0.2,0.22,0.45)
effprob=c(0.05,0.1,0.5,0.68,0.7)

get.oc.backboinet(target_T=target_T, toxprob=toxprob,target_E=target_E,
effprob=effprob,n.dose=5,startdose=1,ncohort=10,cohortsize=3,
pT.saf=0.6 * target_T,pT.tox = 1.4 * target_T,pE.saf = 0.6 * target_E,
alpha.T1=0.5,alpha.E1=0.5,tau.T=1,tau.E=1,te.corr=0.2,
gen.event.time="weibull",accrual=3,gen.enroll.time="uniform",n.earlystop=6,
stopping.npts=12,suspend=0,stopping.prob.T=0.95,stopping.prob.E=0.90,
ppsi01=0,ppsi00=40,ppsi11=60,ppsi10=100,n.sim=2,seed.sim=100)

target_T=0.3
target_E=0.25
toxprob=c(0.03,0.05,0.2,0.22,0.45)
effprob=c(0.05,0.1,0.5,0.68,0.7)

get.oc.backboinet(target_T=target_T, toxprob=toxprob,target_E=target_E,
effprob=effprob,n.dose=5,startdose=1,ncohort=10,cohortsize=3,
pT.saf=0.6 * target_T,pT.tox = 1.4 * target_T,pE.saf = 0.6 * target_E,
alpha.T1=0.5,alpha.E1=0.5,tau.T=1,tau.E=1,te.corr=0.2,
gen.event.time="weibull",accrual=3,gen.enroll.time="uniform",n.earlystop=6,
stopping.npts=12,suspend=0,stopping.prob.T=0.95,stopping.prob.E=0.90,
ppsi01=0,ppsi00=40,ppsi11=60,ppsi10=100,n.sim=2,seed.sim=100)

backboinetr

Description

Obtain the operating characteristics of the backfill bayesian optimal interval design using efficacy and toxicity outcomes for dose optimization within random scenarios

Usage

get.oc.backboinetr(
  target_T = 0.3,
  target_Tr = 0.359,
  target_E = 0.25,
  target_Er = 0.197,
  n.dose,
  startdose,
  ncohort,
  cohortsize,
  pT.saf = 0.6 * target_T,
  pT.tox = 1.4 * target_T,
  pE.saf = 0.6 * target_E,
  alpha.T1 = 0.5,
  alpha.E1 = 0.5,
  tau.T,
  tau.E,
  te.corr = 0.2,
  gen.event.time = "weibull",
  accrual,
  gen.enroll.time = "uniform",
  n.earlystop = 6,
  stopping.npts = 12,
  suspend = 0,
  stopping.prob.T = 0.95,
  stopping.prob.E = 0.9,
  ppsi01 = 0,
  ppsi00 = 40,
  ppsi11 = 60,
  ppsi10 = 100,
  n.sim = 10000,
  seed.sim = 30
)
get.oc.backboinetr(
  target_T = 0.3,
  target_Tr = 0.359,
  target_E = 0.25,
  target_Er = 0.197,
  n.dose,
  startdose,
  ncohort,
  cohortsize,
  pT.saf = 0.6 * target_T,
  pT.tox = 1.4 * target_T,
  pE.saf = 0.6 * target_E,
  alpha.T1 = 0.5,
  alpha.E1 = 0.5,
  tau.T,
  tau.E,
  te.corr = 0.2,
  gen.event.time = "weibull",
  accrual,
  gen.enroll.time = "uniform",
  n.earlystop = 6,
  stopping.npts = 12,
  suspend = 0,
  stopping.prob.T = 0.95,
  stopping.prob.E = 0.9,
  ppsi01 = 0,
  ppsi00 = 40,
  ppsi11 = 60,
  ppsi10 = 100,
  n.sim = 10000,
  seed.sim = 30
)

Arguments

`target_T`	Target toxicity probability. The default value is `target_T=0.3`. When observing 1 DLT out of 3 patients and the target DLT rate is between 0.25 and 0.279, the decision is to stay at the current dose due to a widely accepted practice.
`target_Tr`	The upper boundary for the toxicity when generating the random scenarios. The default value is `target_Tr=0.359`.
`target_E`	The minimum required efficacy probability. The default value is `target_E=0.25`.
`target_Er`	The lower boundary for the efficacy when generating the random scenarios. The default value is `target_Er=0.197`.
`n.dose`	Number of dose.
`startdose`	Starting dose. The lowest dose is generally recommended.
`ncohort`	Number of cohort.
`cohortsize`	Cohort size.
`pT.saf`	Highest toxicity probability that is deemed sub-therapeutic such that dose-escalation should be pursued. The default value is `pT.saf=target_T*0.6`.
`pT.tox`	Lowest toxicity probability that is deemed overly toxic such that dose de-escalation is needed. The default value is `pT.tox=target_T*1.4`.
`pE.saf`	Minimum probability deemed efficacious such that the dose levels with less than delta1 are considered sub-therapeutic. The default value is `pE.saf=target_E*0.6`.
`alpha.T1`	Probability that toxicity event occurs in the late half of toxicity assessment window. The default value is `alpha.T1=0.5`.
`alpha.E1`	Probability that efficacy event occurs in the late half of assessment window. The default value is `alpha.E1=0.5`.
`tau.T`	Toxicity assessment windows (months).
`tau.E`	Efficacy assessment windows (months).
`te.corr`	Correlation between toxicity and efficacy probability, specified as Gaussian copula parameter. The default value is `te.corr=0.2`.
`gen.event.time`	Method to generate the time to first toxicity and efficacy outcome. Weibull distribution is used when `gen.event.time ="weibull"`. Uniform distribution is used when `gen.event.time="uniform"`. The default value is `gen.event.time="weibull"`.
`accrual`	Accrual rate (months) (patient accrual rate per month).
`gen.enroll.time`	Method to generate enrollment time. Uniform distribution is used when `gen.enroll.time="uniform"`. Exponential distribution is used when `gen.enroll.time="exponential"`. The default value is `gen.enroll.time="uniform"`.
`n.earlystop`	the early stopping parameter. If the number of patients treated at the current dose reaches `n.earlystop`,stop the trial and select the optimal dose (OD) based on the observed data.The default value is `n.earlystop=6`.
`stopping.npts`	Early study termination criteria for the number of patients in the dose-escalation and backfill cohorts. If the number of patients at the current dose reaches this criteria and the same dose level is recommended as the next dose level, the study is terminated. The default value is `stopping.npts=12`.
`suspend`	the suspension rule that holds off the decision on dose allocation for the dose-escalation cohort until sufficient toxicity information is available. For example, setting as 0.33 which means one-third of the patients had not completed the toxicity evaluation at the current dose level in the dose escalation cohort. The default value `suspend=0` essentially turns off this type of suspending rule, that is all patients should complete the toxicity evaluation at the current dose level in the dose escalation cohort
`stopping.prob.T`	Early study termination criteria for toxicity, taking a value between 0 and 1. If the posterior probability that toxicity outcome is less than the target toxicity probability (`target_T`) is larger than this criteria, the dose levels are eliminated from the study. The default value is `stopping.prob.T=0.95`.
`stopping.prob.E`	Early study termination criteria for efficacy, taking a value between 0 and 1. If the posterior probability that efficacy outcome is less than the minimum efficacy probability (`target_E`) is larger than this criteria, the dose levels are eliminated from the study. The default value is `stopping.prob.E=0.90`.
`ppsi01`	Score for toxicity=yes and efficacy=no in utility defined by scoring.The default value is `psi01=0`.
`ppsi00`	Score for toxicity=no and efficacy=no in utility defined by scoring. The default value is `psi00=40`.
`ppsi11`	Score for toxicity=yes and efficacy=yes in utility defined by scoring. The default value is `psi11=60`.
`ppsi10`	Score for toxicity=no and efficacy=yes in utility defined by scoring. The default value is `psi10=100`.
`n.sim`	Number of simulated trial. The default value is `n.sim=10000`.
`seed.sim`	Seed for random number generator. The default value is `seed.sim=30`.

Details

The backboinetr is a function which generates the operating characteristics of the backfill bayesian optimal interval design using efficacy and toxicity outcomes for dose optimization by a simulation study. Users can specify a variety of study settings to simulate studies. The operating characteristics of the design are summarized by the percentage of times that each dose level was selected as optimal biological dose and the average number of patients who were treated at each dose level. The percentage of times that the study was terminated and the expected study duration are also provided.

Value

The backboinetr returns a list containing the following components:

`toxprob`	The random true toxicity probability.
`effprob`	The random true efficacy probability.
`phi`	Target toxicity probability.
`delta`	Target efficacy probability.
`target_Tr`	The upper boundary for the toxicity when generating the random scenarios.
`target_Er`	The lower boundary for the efficacy when generating the random scenarios.
`bd.true`	The target optimal dose (OD) level when generating the random scenarios.
`mtd.true`	The maximum tolerated dose (MTD) level when generating the random scenarios.
`lambda1`	Lower toxicity boundary in dose escalation/de-escalation.
`lambda2`	Upper toxicity boundary in dose escalation/de-escalation.
`eta1`	Lower efficacy boundary in dose escalation/de-escalation.
`tau.T`	Toxicity assessment windows (months).
`tau.E`	Efficacy assessment windows (months).
`suspend`	The suspension rule that holds off the decision on dose allocation for the dose-escalation cohort until sufficient toxicity information is available.
`accrual`	Accrual rate (months) (patient accrual rate per month).
`duration`	Expected study duration (months)
`totaln`	Total patients
`data.obs.n`	Record the number of patients in each dose level within the simulations during the trial
`obd`	Record the optimal dose in each simulation during the trial
`backfilltimes`	Record how may times we back-filled during the trial
`backfillcount`	Record the number of back-filled patients in dose level within the simulations during the trial
`PCS`	The percentage of trials that the optimal dose was correctly selected.
`PCA`	The percentage of patients that were correctly allocated to the optimal dose.
`PTS`	The percentage of toxic doses selection.
`PTA`	The percentage of patients who were allocated to toxic doses.

References

BF-BOIN-ET: A backfill Bayesian optimal interval design using efficacy and toxicity outcomes for dose optimization.

Examples


target_T=0.3
target_E=0.25

get.oc.backboinetr(target_T=target_T,target_Tr=0.359,target_E=target_E,
target_Er=0.197,n.dose=5,startdose=1,ncohort=10,cohortsize=3,
pT.saf=0.6 * target_T,pT.tox = 1.4 * target_T,pE.saf = 0.6 * target_E,
alpha.T1=0.5,alpha.E1=0.5,tau.T=1,tau.E=1,te.corr=0.2,
gen.event.time="weibull",accrual=3,gen.enroll.time="uniform",n.earlystop=6,
stopping.npts=12,suspend=0,stopping.prob.T=0.95,stopping.prob.E=0.90,
ppsi01=0,ppsi00=40,ppsi11=60,ppsi10=100,n.sim=2,seed.sim=30)

target_T=0.3
target_E=0.25

get.oc.backboinetr(target_T=target_T,target_Tr=0.359,target_E=target_E,
target_Er=0.197,n.dose=5,startdose=1,ncohort=10,cohortsize=3,
pT.saf=0.6 * target_T,pT.tox = 1.4 * target_T,pE.saf = 0.6 * target_E,
alpha.T1=0.5,alpha.E1=0.5,tau.T=1,tau.E=1,te.corr=0.2,
gen.event.time="weibull",accrual=3,gen.enroll.time="uniform",n.earlystop=6,
stopping.npts=12,suspend=0,stopping.prob.T=0.95,stopping.prob.E=0.90,
ppsi01=0,ppsi00=40,ppsi11=60,ppsi10=100,n.sim=2,seed.sim=30)

Package 'bfboinet'

Help Index

backboinet

Description

Usage

Arguments

Details

Value

References

Examples

backboinetr

Description

Usage

Arguments

Details

Value

References

Examples