get.oc: Generate operating characteristics

Description

Obtain the operating characteristics of the BOIN design by simulating trials.

Usage

get.oc(target, p.true, ncohort, cohortsize, n.earlystop=100, startdose=1, 
p.saf="default", p.tox="default", design = 1, cutoff.eli = 0.95, extrasafe = FALSE, 
offset = 0.05, ntrial = 1000)

Arguments

target

target toxicity rate

p.true

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

ncohort

the total number of cohorts

cohortsize

the cohort size

n.earlystop

Early stopping parameter. If the number of patients treated at the current dose reaches n.earlystop, stop the trial and select the MTD based on the observed data. The default value n.earlystop=100 essentially turns off the type o

startdose

the starting dose level for the trial

p.saf

the highest toxicity probability that is deemed subtherapeutic (i.e. below the MTD) such that dose escalation should be made.The default value is p.saf=0.6xtarget.

p.tox

the loweset toxicity probability that is deemed overly toxic such that deescalation is required. The default value is p.tox=1.4xtarget.

design

to use the local optimal design (the default, design=1) or the global optimal design (design=2).

cutoff.eli

the cutoff to eliminate the overly toxic dose for safety. We recommend the default value of (cutoff.eli=0.95) for general use

extrasafe

set extrasafe=TRUE to impose a more stringent stopping rule

offset

a small positive number (between 0 and 0.5) to control how strict the stopping rule is when extrasafe=TRUE. A larger value leads to a more strict stopping rule. The default value offset=0.05 generally works well.

ntrial

the total number of trials to be simulated.

Value

get.oc returns the operating characteristics of the BOIN design as a data frame, including (1) function arguments (1) selection percentage at each dose level (selpercent), (2) the number of patients treated at each dose level (nptsdose), (3) the number of toxicity observed at each dose level (ntoxdose), (4) the average number of toxicities (totaltox), (5) the average number of patients (totaln), and (6) the percentage of early stopping without selecting the MTD (pctearlystop).

Details

The operating characteristics of the BOIN design are generated by simulating trials under the prespecified true toxicity probabilities of the investigational doses. The BOIN design has two built-in stopping rules: (1) stop the trial if the lowest dose is eliminated due to toxicity, and no dose should be selected as the MTD; and (2) stop the trial and select the MTD if the number of patients treated at the current dose reaches n.earlystop. The first stopping rule is a safety rule to protect patients from the case that all doses are overly toxic. The rationale for the second stopping rule is that when there are a large number (i.e., n.earlystop) of patients assigned to a dose, it means that the dose finding algorithm has approximately converged. Thus, we can early stop the trial and select the MTD to save the sample size and reduce the trial duration.

For some applications, investigators may prefer a more strict safety stopping rule than rule (1) for extra safety when the lowest dose is overly toxic. Setting extrasafe=TRUE imposes the following more strict safety stopping rule: stop the trial if (i) the number of patients treated at the lowest dose >=3, and (ii) Pr(toxicity rate of the lowest dose > target | data) > cutoff.eli-offset. As a tradeoff, the strong stopping rule will decrease the selection percentage of the MTD when the lowest dose actually is the MTD.

References

Liu S. and Yuan, Y. (2015). Bayesian Optimal Interval Designs for Phase I Clinical Trials, Journal of the Royal Statistical Society: Series C, in press

Examples

Run this code

## Consider a phase I trial aiming to find the MTD with a target toxicity rate of 0.3 
## the maximum sample size is 30 patients in cohort size of 3
## assume the true toxicity rates of 5 doses are (0.05, 0.15, 0.3, 0.45, 0.6)
## run 1,000 simulated trials
ptox = c(0.05, 0.15, 0.3, 0.45, 0.6)
get.oc(target=0.3, p.true=ptox, ncohort=10, cohortsize=3, ntrial=1000)

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