% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/select_mtd.R
\name{select_mtd}
\alias{select_mtd}
\title{select_mtd}
\usage{
select_mtd(
  target = 0.3,
  npts,
  ntox,
  cutoff.eli = 0.95,
  extrasafe = FALSE,
  offset = 0.05,
  p.tox = 1.4 * target,
  boundMTD = FALSE,
  n.elimination = 3
)
}
\arguments{
\item{target}{Target toxicity probability. The default value is \code{target_T=0.3}.}

\item{npts}{The number of patients enrolled at each dose level.}

\item{ntox}{Number of patients with dose limiting toxicity (DLT).}

\item{cutoff.eli}{The cutoff to eliminate an overly toxic dose for safety.
We recommend the default value of (\code{cutoff.eli=0.95}) for general use.}

\item{extrasafe}{Set \code{extrasafe=TRUE} to impose a more stringent stopping rule.
The default value is \code{extrasafe=FALSE}.}

\item{offset}{A small positive number (between 0 and 0.5) to control how strict the
stopping rule is when \code{extrasafe=TRUE}. A larger value leads to a more
strict stopping rule. The default value \code{offset=0.05} generally works well.}

\item{p.tox}{the lowest toxicity probability that is deemed overly toxic such
that deescalation is required. The default value is \code{p.tox=1.4*target}.}

\item{boundMTD}{set \code{boundMTD=TRUE} to impose the condition: the isotonic 
estimate of toxicity probability for the selected MTD must be less than 
de-escalation boundary. The default value is \code{boundMTD=FALSE}.}

\item{n.elimination}{The sample size cutoff for elimination. 
The default is \code{n.elimination=3}.}
}
\value{
\code{select_mtd()} returns the selected dose.
}
\description{
Obtain the maximum tolerated dose (MTD) of the backfill 
bayesian optimal interval design using efficacy and toxicity outcomes.
}
\examples{
target<-0.3
y<-c(0,0,1,2,3,0)
n<-c(3,3,6,9,9,0)
select_mtd(target=target,npts=n,ntox=y)

}
\references{
1. 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.
2. Yuan, Y., Hess, K. R., Hilsenbeck, S. G., & Gilbert, M. R. (2016). Bayesian optimal interval design: a simple and well-performing design for phase I oncology trials. Clinical Cancer Research, 22(17), 4291-4301.
3. Zhou, H., Yuan, Y., & Nie, L. (2018). Accuracy, safety, and reliability of novel phase I trial designs. Clinical Cancer Research, 24(18), 4357-4364.
4. Zhou, Y., Lin, R., Kuo, Y. W., Lee, J. J., & Yuan, Y. (2021). BOIN Suite: A Software Platform to Design and Implement Novel Early-Phase Clinical Trials. JCO Clinical Cancer Informatics, 5, 91-101.
5. Takeda K, Xia Q, Liu S, Rong A. TITE-gBOIN: Time-to-event Bayesian optimal interval design to accelerate dose-finding accounting for toxicity grades. Pharm Stat. 2022 Mar;21(2):496-506. doi: 10.1002/pst.2182. Epub 2021 Dec 3. PMID: 34862715.
6. Yuan, Y., Lin, R., Li, D., Nie, L. and Warren, K.E. (2018). Time-to-event Bayesian Optimal Interval Design to Accelerate Phase I Trials. Clinical Cancer Research, 24(20): 4921-4930.
7. Rongji Mu, Ying Yuan, Jin Xu, Sumithra J. Mandrekar, Jun Yin, gBOIN: A Unified Model-Assisted Phase I Trial Design Accounting for Toxicity Grades, and Binary or Continuous End Points, Journal of the Royal Statistical Society Series C: Applied Statistics, Volume 68, Issue 2, February 2019, Pages 289–308, https://doi.org/10.1111/rssc.12263.
8. Lin R, Yuan Y. Time-to-event model-assisted designs for dose-finding trials with delayed toxicity. Biostatistics. 2020 Oct 1;21(4):807-824. doi: 10.1093/biostatistics/kxz007. PMID: 30984972; PMCID: PMC8559898.
9. Hsu C, Pan H, Mu R (2022). _UnifiedDoseFinding: Dose-Finding Methods for Non-Binary Outcomes_. R package version 0.1.9, <https://CRAN.R-project.org/package=UnifiedDoseFinding>.
}
