tite.boinet {boinet}R Documentation

Conducting simulation study of TITE-BOIN-ET design

Description

Time-to-event Bayesian optimal interval design to accelerate dose-finding based on both efficacy and toxicity outcomes (TITE-BOIN-ET design) is implemented under a scenario specified. 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.

Usage

tite.boinet(
  n.dose, start.dose, size.cohort, n.cohort,
  toxprob, effprob,
  phi=0.3, phi1=phi*0.1, phi2=phi*1.4, delta=0.6, delta1=delta*0.6,
  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",
  stopping.npts=size.cohort*n.cohort,
  stopping.prob.T=0.95, stopping.prob.E=0.99,
  estpt.method, obd.method,
  w1= 0.33, w2=1.09,
  plow.ast=phi1, pupp.ast=phi2, qlow.ast=delta1/2, qupp.ast=delta,
  psi00=40, psi11=60,
  n.sim=1000, seed.sim=100)

Arguments

n.dose

Number of dose.

start.dose

Starting dose. The lowest dose is generally recommended.

size.cohort

Cohort size.

n.cohort

Number of cohort.

toxprob

Vector of true toxicity probability.

effprob

Vector of true efficacy probability.

phi

Target toxicity probability. The default value is phi=0.3.

phi1

Highest toxicity probability that is deemed sub-therapeutic such that dose-escalation should be pursued. The default value is phi1=phi*0.1.

phi2

Lowest toxicity probability that is deemed overly toxic such that dose de-escalation is needed. The default value is phi2=phi*1.4.

delta

Target efficacy probability. The default value is delta=0.6.

delta1

Minimum probability deemed efficacious such that the dose levels with less than delta1 are considered sub-therapeutic. The default value is delta1=delta*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 (days).

tau.E

Efficacy assessment windows (days).

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 (days) (average number of days necessary to enroll one patient).

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".

stopping.npts

Early study termination criteria for the number of patients. If the number of patients at the current dose reaches this criteria, the study is terminated. The default value is stopping.npts=size.cohort*n.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 (phi) 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 (delta1) is larger than this criteria, the dose levels are eliminated from the study. The default value is stopping.prob.E=0.99.

estpt.method

Method to estimate the efficacy probability. Fractional polynomial logistic regression is used when estpt.method="fp.logistic". Model averaging of multiple unimodal isotopic regression is used when estpt.method="multi.iso". Observed efficacy probability is used when estpt.method="obs.prob".

obd.method

Method to select the optimal biological dose. Utility defined by weighted function is used when obd.method="utility.weighted". Utility defined by truncated linear function is used when obd.method="utility.truncated.linear". Utility defined by scoring is used when obd.method="utility.scoring". Highest estimated efficacy probability is used when obd.method="max.effprob".

w1

Weight for toxicity-efficacy trade-off in utility defined by weighted function. This must be specified when using obd.method="utility.weighted". The default value is w1=0.33.

w2

Weight for penalty imposed on toxic doses in utility defined by weighted function. This must be specified when using obd.method="utility.weighted". The default value is w2=1.09.

plow.ast

Lower threshold of toxicity linear truncated function. This must be specified when using obd.method="utility.truncated.linear". The default value is plow.ast=phi1.

pupp.ast

Upper threshold of toxicity linear truncated function. This must be specified when using obd.method="utility.truncated.linear". The default value is pupp.ast=phi2.

qlow.ast

Lower threshold of efficacy linear truncated function. This must be specified when using obd.method="utility.truncated.linear". The default value is qlow.ast=delta1/2.

qupp.ast

Upper threshold of efficacy linear truncated function. This must be specified when using obd.method="utility.truncated.linear". The default value is qupp.ast=delta.

psi00

Score for toxicity=no and efficacy=no in utility defined by scoring. This must be specified when using obd.method="utility.scoring". The default value is psi00=40.

psi11

Score for toxicity=yes and efficacy=yes in utility defined by scoring. This must be specified when using obd.method="utility.scoring". The default value is psi11=60.

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 tite.boinet is a function which generates the operating characteristics of the time-to-event Bayesian optimal interval design to accelerate dose-finding based on both efficacy and toxicity outcomes (TITE-BOIN-ET design) by a simulation study. Users can specify a variety of study settings to simulate studies, and choose methods to estimate the efficacy probability and to select the optimal biological dose. 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 tite.boinet 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 (days).

tau.E

Efficacy assessment windows (days).

accrual

Accrual rate (days) (average number of days necessary to enroll one patient).

estpt.method

Method to estimate the efficacy probability.

obd.method

Method to select the optimal biological dose.

n.patient

Average number of 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 (days)

References

Takeda K, Morita S, Taguri M. TITE-BOIN-ET: Time-to-event Bayesian optimal interval design to accelerate dose-finding based on both efficacy and toxicity outcomes. Pharmaceutical Statistics 2020; 19(3):335-349.

Yamaguchi Y, Takeda K, Yoshida S, Maruo K. Optimal biological dose selection in dose-finding trials with model-assisted designs based on efficacy and toxicity: a simulation study. Journal of Biopharmaceutical Statistics 2023; doi: 10.1080/10543406.2023.2202259.

Examples

n.dose      <- 6
start.dose  <- 1
size.cohort <- 3
n.cohort    <- 12

toxprob <- c(0.01,0.03,0.06,0.12,0.18,0.30)
effprob <- c(0.06,0.08,0.15,0.25,0.40,0.80)

phi   <- 0.33
delta <- 0.70

tau.T   <- 30
tau.E   <- 45
accrual <- 10

estpt.method <- "obs.prob"
obd.method   <- "max.effprob"

n.sim <- 10

tite.boinet(
  n.dose=n.dose, start.dose=start.dose,
  size.cohort=size.cohort, n.cohort=n.cohort,
  toxprob=toxprob, effprob=effprob,
  phi=phi, delta=delta,
  tau.T=tau.T, tau.E=tau.E, accrual=accrual,
  estpt.method=estpt.method, obd.method=obd.method,
  n.sim=n.sim)

[Package boinet version 1.0.0 Index]