Description Usage Arguments Details Value Note Author(s) References See Also Examples

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

1 | ```
select.mtd(target, npts, ntox, cutoff.eli=0.95, extrasafe=FALSE, offset=0.05,verbose=TRUE)
``` |

`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 ( |

`extrasafe` |
set |

`offset` |
a small positive number (between |

`verbose` |
set |

`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).

`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`

)

The MTD selection and dose escalation/deescalation rule are two independent components of the trial design. When appropriate, another dose selection procedure (e.g., based on a fitted logistic model) can be used to select the MTD after the completion of the trial using the BOIN design.

Suyu Liu 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, 507-523.

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.

Tutorial: http://odin.mdacc.tmc.edu/~yyuan/Software/BOIN/BOIN2.6_tutorial.pdf

Paper: http://odin.mdacc.tmc.edu/~yyuan/Software/BOIN/paper.pdf

1 2 3 4 5 6 | ```
### 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.boin(selmtd)
plot.boin(selmtd)
``` |

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