next.subtrial {BOIN}

Determine the starting dose and the dose-searching space for next subtrial in waterfall design

Description

Determine the starting dose and the dose-searching space for next subtrial after the current subtrial is completed when using the waterfall design

Usage

next.subtrial(
  target,
  npts,
  ntox,
  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 who experienced dose-limiting toxicities at each dose combination

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

For the waterfall design, this function is used to obtain the starting dose and dose-searching space for the next subtrial when the current subtrial is completed. The input 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).

Value

next.subtrial() returns a list object, containing the starting dose ($starting_dose) and the dose-searching space for the the next subtrial ($next_subtrial).

Author(s)

Suyu Liu, Liangcai Zhang 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>.

Zhang L. and Yuan, Y. (2016). A Simple Bayesian Design to Identify the Maximum Tolerated Dose Contour for Drug Combination Trials, Statistics in Medicine, 35, 4924-4936.

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 starting dose and dose searching space for next subtrial
n <- matrix(c(6, 0, 0, 0,
           6, 10, 12, 0,
           9, 12, 0, 0), ncol=4, byrow=TRUE)
y <- matrix(c(0, 0, 0, 0,
            1, 1, 4, 0,
            2, 3, 0, 0), ncol=4, byrow=TRUE)
nxt.trial <- next.subtrial(target=0.3, npts=n, ntox=y)
summary(nxt.trial)