Keyboard (version 0.1.3)

get.oc.obd.kb: Generate Operating Characteristics to Find the Optimal Biological Dose

Description

This function generates operating characteristics to find the optimal biological dose (OBD).

Usage

get.oc.obd.kb(
  toxicity.low,
  toxicity.moderate,
  toxicity.high,
  efficacy.low,
  efficacy.moderate,
  efficacy.high,
  target.toxicity,
  target.efficacy,
  ncohort = 10,
  cohortsize = 3,
  n.early = 100,
  startdose = 1,
  p.true,
  q.true,
  ntrial = 1000,
  seed = 6,
  p1 = 0.15,
  p2 = 0.4,
  q1 = 0.3,
  q2 = 0.6,
  cutoff.eli.toxicity = 0.95,
  cutoff.eli.efficacy = 0.3,
  w1.toxicity = 0.33,
  w2.toxicity = 1.09,
  indicator = target.toxicity
)

Value

get.oc.obd.kb() returns the operating characteristics of the Keyboard design as a list, including:

1. the selection percentage at each dose level using utility function 1 ($selpercent1),
   

2. the selection percentage at each dose level using utility function 2 ($selpercent2),
   

3. the selection percentage at each dose level using utility function 3 ($selpercent3),
   

4. the number of patients treated at each dose level ($npatients),
   

5. the number of dose-limiting toxicities (DLTs) observed at each dose level ($ntox),
   

6. the number of responses observed at each dose level ($neff),
   

7. the average number of DLTs ($totaltox),
   

8. the average number of responses ($totaleff),
   

9. the average number of patients ($totaln),
   

10. the percentage of early stopping without selecting the OBD using utility function 1 ($percentstop1),
    

11. the percentage of early stopping without selecting the OBD using utility function 2 ($percentstop2),
    

12. the percentage of early stopping without selecting the OBD using utility function 3 ($percentstop3),
    

13. data.frame ($simu.setup) containing simulation parameters, such as target, p.true, etc.

Arguments

toxicity.low

The upper boundary for the low toxicity interval.

toxicity.moderate

The upper boundary for the moderate toxicity interval.

toxicity.high

The upper boundary for the high toxicity interval.

efficacy.low

The upper boundary for the low efficacy interval.

efficacy.moderate

The upper boundary for the moderate efficacy interval.

efficacy.high

The upper boundary for the high efficacy interval.

target.toxicity

The target DLT rate.

target.efficacy

The target efficacy rate.

ncohort

The total number of cohorts.

cohortsize

The number of patients in the cohort.

n.early

The early stopping parameter. If the number of patients treated at the current dose reaches n.early, then we stop the trial and select the optimal biological dose (OBD) based on the observed data. The default value is 100.

startdose

The starting dose level.

p.true

A vector containing the true toxicity probabilities of the investigational dose levels.

q.true

A vector containing the true efficacy probabilities of the investigational dose levels.

ntrial

The total number of trials to be simulated.

seed

The random seed for simulation.

p1

The cutoff lower limit for safety utility function 1, described in the Details section.

p2

The cutoff upper limit for safety utility function 1, described in the Details section.

q1

The cutoff lower limit for efficacy utility function 1, described in the Details section.

q2

The cutoff upper limit for efficacy utility function 1, described in the Details section.

cutoff.eli.toxicity

The cutoff value to eliminate a dose with unacceptable high toxicity for safety. The default value is 0.95.

cutoff.eli.efficacy

The cutoff value for the futility rule, the acceptable lowest efficacy. The default value is 0.30.

w1.toxicity

The weight for toxicity utility function 2 and 3,described in the Details section.

w2.toxicity

The weight for toxicity utility function 3, described in the Details section.

indicator

The indicator cutoff value for utility function 3, described in the Details section.

Author

Xiaomeng Yuan, Chen Li, Hongying Sun, Li Tang and Haitao Pan

Details

Large trials are simulated to characterize the operating characteristics of the Keyboard design under the prespecified true toxicity probabilities and true efficacy probabilities of the investigational doses. The dose assignment follows the rules described in the function get.decision.obd.kb().

The following stopping rules are built in the Keyboard design:

1. Stop the trial if the lowest dose is eliminated from the trial due to high unacceptable toxicity.

2. Stop the trial if the number of patients treated at the current dose exceeds n.earlystop.

References

Li DH, Whitmore JB, Guo W, Ji Y. Toxicity and efficacy probability interval design for phase I adoptive cell therapy dose-finding clinical trials. Clinical Cancer Research. 2017; 23:13-20. https://clincancerres.aacrjournals.org/content/23/1/13.long

Liu S, Johnson VE. A robust Bayesian dose-finding design for phase I/II clinical trials. Biostatistics. 2016; 17(2):249-63. https://academic.oup.com/biostatistics/article/17/2/249/1744018

Zhou Y, Lee JJ, Yuan Y. A utility-based Bayesian optimal interval (U-BOIN) phase I/II design to identify the optimal biological dose for targeted and immune therapies. Statistics in Medicine. 2019; 38:S5299-5316. https://onlinelibrary.wiley.com/doi/epdf/10.1002/sim.8361

See Also

Other single-agent phase I/II functions: get.decision.obd.kb(), get.decision.obd2.kb(), get.oc.obd2.kb(), select.obd.kb()

Examples

# \donttest{
toxicity.low <- 0.15
toxicity.moderate <- 0.25
toxicity.high <- 0.35
efficacy.low <- 0.25
efficacy.moderate <- 0.45
efficacy.high <- 0.65
target.toxicity <- 0.30
target.efficacy <- 0.40
p.true <-c(0.08,0.30,0.60,0.80)
q.true <- c(0.25,0.40,0.25,0.50)
oc.obd.kb <- get.oc.obd.kb(toxicity.low = toxicity.low,
             toxicity.moderate= toxicity.moderate,
             toxicity.high = toxicity.high,
             efficacy.low = efficacy.low,
             efficacy.moderate = efficacy.moderate,
             efficacy.high = efficacy.high,
             target.toxicity=target.toxicity,
             target.efficacy= target.efficacy,
             p.true= p.true, q.true= q.true)
oc.obd.kb
summary_kb(oc.obd.kb)
plot_kb(oc.obd.kb)
plot_kb(oc.obd.kb$selpercent1)
plot_kb(oc.obd.kb$selpercent2)
plot_kb(oc.obd.kb$selpercent3)
plot_kb(oc.obd.kb$npatients)
plot_kb(oc.obd.kb$ntox)
plot_kb(oc.obd.kb$neff)
# }