scaled

dose

A numeric exponent for power-law rescaling

Implement an inverse power-law scaling for drug dose.

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>.

David C. Norris

DTAT

Dose Titration Algorithm Tuning

scaled function

<dl><dt>dose</dt>
<dd>A numeric vector of doses</dd>
<dt>a</dt>
<dd>A numeric exponent for power-law rescaling</dd></dl>

Arguments

Author

Power-law scaling for doses — scaled

<dl>

<dt>dose</dt>
<dd>A numeric vector of doses</dd>


<dt>a</dt>
<dd>A numeric exponent for power-law rescaling</dd>

</dl>

scaled: Power-law scaling for doses

Description

Usage

Value

Arguments

Author