BOIN (version 2.7.2)

select.mtd: Select the maximum tolerated dose (MTD) for single agent trials

Description

Select the maximum tolerated dose (MTD) when the single-agent trial is completed

Usage

select.mtd(target, npts, ntox, cutoff.eli=0.95, extrasafe=FALSE, offset=0.05,
                 boundMTD=FALSE,p.tox=1.4*target)

Arguments

target

the target DLT rate

npts

a vector containing the number of patients treated at each dose level

ntox

a vector containing the number of patients who experienced dose-limiting toxicity at each dose level

cutoff.eli

the cutoff to eliminate overly toxic doses for safety. We recommend the default value of (cutoff.eli=0.95) for general use.

extrasafe

set extrasafe=TRUE to impose a more strict stopping rule for extra safety

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.

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.

Value

select.mtd() returns (1) target toxicity probability ($target), (2) selected MTD ($MTD), (3) isotonic estimate of the DLT probablity at each dose and associated \(95\%\) credible interval ($p_est), and (4) the probability of overdosing defined as \(Pr(toxicity>\code{target}|data)\) ($p_overdose)

Details

select.mtd() selects the MTD based on isotonic estimates of toxicity probabilities. select.mtd() selects as the MTD dose \(j^*\), for which the isotonic estimate of the DLT rate is closest to the target. If there are ties, we select from the ties the highest dose level when the estimate of the DLT rate is smaller than the target, or the lowest dose level when the estimate of the DLT rate is greater than the target. The isotonic estimates are obtained by the pooled-adjacent-violators algorithm (PAVA) (Barlow, 1972).

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

# NOT RUN {
### select the MTD for BOIN single agent trial
n <- c(3, 3, 15, 9, 0)
y <- c(0, 0, 4, 4, 0)
selmtd <- select.mtd(target=0.3, npts=n, ntox=y)
summary(selmtd)
plot(selmtd)

# }