select.mtd {BOIN}  R Documentation 
Select the maximum tolerated dose (MTD) for single agent trials
Description
Select the maximum tolerated dose (MTD) when the singleagent 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 doselimiting 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 
boundMTD 
set 
p.tox 
the lowest toxicity probability that is deemed overly toxic such
that deescalation is required. The default value is

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 pooledadjacentviolators algorithm
(PAVA) (Barlow, 1972).
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
)
Note
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.
Author(s)
Suyu Liu, Yanhong Zhou, 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, 507523.
Yan, F., Zhang, L., Zhou, Y., Pan, H., Liu, S. and Yuan, Y. (2020).BOIN: An R Package for Designing SingleAgent and DrugCombination DoseFinding Trials Using Bayesian Optimal Interval Designs. Journal of Statistical Software, 94(13),132.<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 Wellperforming Design for Phase I Oncology Trials, Clinical Cancer Research, 22, 42914301.
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
### 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)