DTAT: Dose Titration Algorithm Tuning

Dose Titration Algorithm Tuning (DTAT) is a methodologic framework allowing dose individualization to be conceived as a continuous learning process that begins in early-phase clinical trials and continues throughout drug development, on into clinical practice. This package includes code that researchers may use to reproduce or extend key results of the DTAT research programme, plus tools for trialists to design and simulate a '3+3/PC' dose-finding study. Please see Norris (2017a) <doi:10.12688/f1000research.10624.3> and Norris (2017c) <doi:10.1101/240846>.

Version: 0.3-7
Depends: R (≥ 3.5.0), survival
Imports: km.ci, pomp, Hmisc, data.table, dplyr, r2d3, shiny, jsonlite, methods
Suggests: knitr, rmarkdown, lattice, latticeExtra, widgetframe, tidyr, RColorBrewer, invgamma, zipfR, rms
Published: 2024-05-25
DOI: 10.32614/CRAN.package.DTAT
Author: David C. Norris [aut, cre]
Maintainer: David C. Norris <david at precisionmethods.guru>
License: MIT + file LICENSE
URL: https://precisionmethods.guru/
NeedsCompilation: no
Citation: DTAT citation info
In views: ClinicalTrials
CRAN checks: DTAT results


Reference manual: DTAT.pdf
Vignettes: Exploring the '3+3/PC' dose-titration design
Code and figures for 'DTAT should supersede MTD' v1


Package source: DTAT_0.3-7.tar.gz
Windows binaries: r-devel: DTAT_0.3-7.zip, r-release: DTAT_0.3-7.zip, r-oldrel: DTAT_0.3-7.zip
macOS binaries: r-release (arm64): DTAT_0.3-7.tgz, r-oldrel (arm64): DTAT_0.3-7.tgz, r-release (x86_64): DTAT_0.3-7.tgz, r-oldrel (x86_64): DTAT_0.3-7.tgz
Old sources: DTAT archive


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