get.oc: Generate operating characteristics for single agent trials

In BOIN: Bayesian Optimal INterval (BOIN) Design for Single-Agent and Drug- Combination Phase I Clinical Trials

Description

Obtain the operating characteristics of the BOIN design for single agent trials by simulating trials.

Usage

get.oc(target, p.true, ncohort, cohortsize, n.earlystop=100,
              startdose=1, titration=FALSE, p.saf=0.6*target, p.tox=1.4*target,
              cutoff.eli=0.95,extrasafe=FALSE, offset=0.05, boundMTD=FALSE,
              ntrial=1000, seed=6)

Arguments

target	the target DLT 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	the 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 this type of early stopping.
startdose	the starting dose level for the trial
titration	set titration=TRUE to perform dose escalation with cohort size = 1 to accelerate dose escalation at the begining of the trial.
p.saf	the highest toxicity probability that is deemed subtherapeutic (i.e. below the MTD) such that dose escalation should be undertaken. The default value is p.saf=0.6*target.
p.tox	the lowest toxicity probability that is deemed overly toxic such that deescalation is required. The default value is p.tox=1.4*target).
cutoff.eli	the cutoff to eliminate an 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.
boundMTD	set boundMTD=TRUE to impose the condition: the isotonic estimate of toxicity probability for the selected MTD must be less than de-escalation boundary.
ntrial	the total number of trials to be simulated
seed	the random seed for simulation

Details

The operating characteristics of the BOIN design are generated by simulating trials under the prespecified true toxicity probabilities of the investigational doses. If titration=TRUE, we perform dose escalation with cohort size = 1 at the begining of the trial: starting from startdose, if no toxicity is observed, we escalate the dose; otherwise, the titration is completed and we switch to cohort size = cohortsize. Titration accelerates the dose escalation and is useful when low doses are believed to be safe.

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 in which all doses are overly toxic. The rationale for the second stopping rule is that when there is 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 stop the trial early and select the MTD to save 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. This can be achieved by setting extrasafe=TRUE, which 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 > \code{target} | data) > \code{cutoff.eli}-\code{offset}. As a tradeoff, the strong stopping rule will decrease the MTD selection percentage when the lowest dose actually is the MTD.

Value

get.oc() returns the operating characteristics of the BOIN design as a list, including: (1) selection percentage at each dose level ($selpercent), (2) the number of patients treated at each dose level ($npatients), (3) the number of toxicities observed at each dose level ($ntox), (4) the average number of toxicities ($totaltox), (5) the average number of patients ($totaln), (6) the percentage of early stopping without selecting the MTD ($percentstop), (7) risk of overdosing 60% or more of patients ($overdose60), (8) risk of overdosing 80% or more of patients ($overdose80), (9) data.frame ($simu.setup) containing simulation parameters, such as target, p.true, etc.

Note

We should avoid setting the values of p.saf and p.tox very close to the target. This is because the small sample sizes of typical phase I trials prevent us from differentiating the target DLT rate from the rates close to it. The default values provided by get.oc() are strongly recommended, and generally yield excellent operating characteristics.

Author(s)

Suyu Liu, Yanhong Zhou, and Ying Yuan

References

Liu S. and Yuan, Y. (2015). Bayesian Optimal Interval Designs for Phase I Clinical Trials, Journal of the Royal Statistical Society: Series C, 64, 507-523.

Yan, F., Zhang, L., Zhou, Y., Pan, H., Liu, S. and Yuan, Y. (2020).BOIN: An R Package for Designing Single-Agent and Drug-Combination Dose-Finding Trials Using Bayesian Optimal Interval Designs. Journal of Statistical Software, 94(13),1-32.<doi:10.18637/jss.v094.i13>.

Yuan Y., Hess K.R., Hilsenbeck S.G. and Gilbert M.R. (2016) Bayesian Optimal Interval Design: A Simple and Well-performing Design for Phase I Oncology Trials, Clinical Cancer Research, 22, 4291-4301.

See Also

Tutorial: http://odin.mdacc.tmc.edu/~yyuan/Software/BOIN/BOIN2.6_tutorial.pdf

Paper: http://odin.mdacc.tmc.edu/~yyuan/Software/BOIN/paper.pdf

Examples

## get the operating characteristics for BOIN single agent trial
oc <- get.oc(target=0.3, p.true=c(0.05, 0.15, 0.3, 0.45, 0.6),
			ncohort=20, cohortsize=3, ntrial=1000)

summary(oc) # summarize design operating characteristics
plot(oc)   # plot flowchart of the BOIN design and design operating characteristics, including
           # selection percentage, number of patients, and observed toxicities at each dose


## perform titration at the begining of the trial to accelerate dose escalation
oc <- get.oc(target=0.3, p.true=c(0.05, 0.15, 0.3, 0.45, 0.6),
			titration=TRUE, ncohort=20, cohortsize=3, ntrial=1000)

summary(oc)          # summarize design operating characteristics
plot(oc)  # plot flowchart of the BOIN design and design operating characteristics

BOIN documentation built on Jan. 20, 2021, 1:06 a.m.