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 (2017) <doi:10.12688/f1000research.10624.3> and
Norris (2017) <doi:10.1101/240846>.
| Version: |
0.3-4 |
| Depends: |
R (≥ 3.4.0), survival |
| Imports: |
km.ci, pomp, Hmisc, data.table, dplyr, r2d3, shiny, jsonlite, methods |
| Suggests: |
knitr, rmarkdown, lattice, latticeExtra, widgetframe, tidyr, RColorBrewer |
| Published: |
2020-06-14 |
| 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 |
Documentation:
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