get.oc {BOIN}  R Documentation 
Obtain the operating characteristics of the BOIN design for single agent trials by simulating trials.
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)
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 
startdose 
the starting dose level for the trial 
titration 
set 
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.tox 
the lowest toxicity probability that is deemed overly toxic such
that deescalation is required. The default value is

cutoff.eli 
the cutoff to eliminate an overly toxic dose for safety.
We recommend the default value of ( 
extrasafe 
set 
offset 
a small positive number (between 
boundMTD 
set 
ntrial 
the total number of trials to be simulated 
seed 
the random seed for simulation 
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 builtin 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 dosefinding 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.
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.
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.
Suyu Liu, Yanhong Zhou, and Ying Yuan
Liu S. and Yuan, Y. (2015). Bayesian Optimal Interval Designs for Phase I Clinical Trials, Journal of the Royal Statistical Society: Series C, 64, 507523.
Yan, F., Zhang, L., Zhou, Y., Pan, H., Liu, S. and Yuan, Y. (2020).BOIN: An R Package for Designing SingleAgent and DrugCombination DoseFinding Trials Using Bayesian Optimal Interval Designs. Journal of Statistical Software, 94(13),132.<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 Wellperforming Design for Phase I Oncology Trials, Clinical Cancer Research, 22, 42914301.
Tutorial: http://odin.mdacc.tmc.edu/~yyuan/Software/BOIN/BOIN2.6_tutorial.pdf
Paper: http://odin.mdacc.tmc.edu/~yyuan/Software/BOIN/paper.pdf
## 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