next.comb {BOIN}

Determine the dose combination for the next cohort of new patients for drug-combination trials that aim to find a MTD

Description

Determine the dose combination for the next cohort of new patients for drug-combination trials that aim to find a MTD

Usage

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)

Arguments

target

the target DLT rate

npts

a J*K matrix (J<=K) containing the number of patients treated at each dose combination

ntox

a J*K matrix (J<=K) containing the number of patients experienced dose-limiting toxicity at each dose combination

dose.curr

the current dose combination

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.

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.

Details

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).

Value

the recommended dose for treating the next cohort of patients ($next_dc).

Author(s)

Suyu Liu 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.

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>.

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

## 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)