Determine the dose combination for the next cohort of new patients for drug-combination trials that aim to find a MTD
next.comb(target, npts, ntox, dose.curr, n.earlystop=100,
p.saf=0.6*target, p.tox=1.4*target, cutoff.eli=0.95,
extrasafe=FALSE, offset=0.05)
the target DLT rate
a J*K
matrix (J<=K)
containing the number of patients treated at each dose combination
a J*K
matrix (J<=K)
containing the number of patients experienced
dose-limiting toxicity at each dose combination
the current dose combination
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.
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
.
the lowest toxicity probability that is deemed overly toxic such
that deescalation is required. The default value is
p.tox=1.4*target
.
the cutoff to eliminate an overly toxic dose for safety.
We recommend the default value of (cutoff.eli=0.95
)
for general use.
set extrasafe=TRUE
to impose a more stringent stopping rule
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.
the recommended dose for treating the next cohort of patients ($next_dc
).
This function is used to determine dose combination for conducting combination trials.
Given the currently observed data, next.comb()
determines dose combination for
treating the next cohort of new patients. The currently observed data include: the
number of patients treated at each dose combination (i.e., npts
),
the number of patients who experienced dose-limiting toxicities at each dose
combination (i.e., ntox
), and the level of current dose (i.e., dose.curr
).
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.
Lin R. and Yin, G. (2017). Bayesian Optimal Interval Designs for Dose Finding in Drug-combination Trials, Statistical Methods in Medical Research, 26, 2155-2167.
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>.
Tutorial: http://odin.mdacc.tmc.edu/~yyuan/Software/BOIN/BOIN2.6_tutorial.pdf
Paper: http://odin.mdacc.tmc.edu/~yyuan/Software/BOIN/paper.pdf
# NOT RUN {
## determine the dose combination for the next cohort of new patients
n <- matrix(c(3, 0, 0, 0, 0, 7, 6, 0, 0, 0, 0, 0, 0, 0, 0), ncol=5, byrow=TRUE)
y <- matrix(c(0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0), ncol=5, byrow=TRUE)
nxt.comb <- next.comb(target=0.3, npts=n, ntox=y, dose.curr=c(2, 2))
summary(nxt.comb)
# }
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