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) .


Reference manual

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

Browse source code at

Authors: David C. Norris [aut, cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

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

Depends on survival

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

See at CRAN