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) and Norris (2017) .


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install.packages("DTAT")

0.3-1 by David C. Norris, 3 months ago


https://osf.io/5479p/


Browse source code at https://github.com/cran/DTAT


Authors: David C. Norris [aut, cre]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports km.ci, pomp, Hmisc, data.table, dplyr, r2d3, shiny, jsonlite, methods

Depends on survival

Suggests knitr, rmarkdown, lattice, latticeExtra, widgetframe, tidyr, RColorBrewer


See at CRAN