NextBestInfTheory-class

.NextBestInfTheory

NextBestInfTheory

.DefaultNextBestInfTheory

<a href="https://lifecycle.r-lib.org/articles/stages.html#stable"><img src="figures/lifecycle-stable.svg?package=crmPack&version=2.0.0" alt="[Stable]"></a>
<code>NextBestInfTheory</code> is the class for next best dose that is based on
information theory as proposed in https://doi.org/10.1002/sim.8450.

Implements a wide range of dose escalation
designs. The focus is on model-based designs, ranging from classical and
modern continual reassessment methods (CRMs) based on dose-limiting toxicity
endpoints to dual-endpoint designs taking into account a biomarker/efficacy
outcome. Bayesian inference is performed via MCMC sampling in JAGS, and it is easy
to setup a new design with custom JAGS code. However, it is also possible to
implement 3+3 designs for comparison or models with non-Bayesian estimation.
The whole package is written in a modular form in the S4 class system, making it
very flexible for adaptation to new models, escalation or stopping rules.
Further details are presented in
Sabanes Bove et al. (2019) <doi:10.18637/jss.v089.i10>.

Daniel Sabanes Bove

crmPack

Object-Oriented Implementation of Dose Escalation Designs

Wai Yin Yeung

Burak Kuersad Guenhan

Giuseppe Palermo

Thomas Jaki

Jiawen Zhu

Ziwei Liao

Dimitris Kontos

Marlene Schulte-Goebel

Doug Kelkhoff

Oliver Boix

Robert Adams

Clara Beck

John Kirkpatrick

Wojciech Wójciak

Guanya Peng

Prerana Chandratre

F. Hoffmann-La Roche AG 

Merck Healthcare KGaA 

Bayer AG 

RPACT GmbH 

NextBestInfTheory-class function

<dl><dt>target</dt>
<dd>(<code>proportion</code>) see slot definition.</dd>
<dt>asymmetry</dt>
<dd>(<code>number</code>) see slot definition.</dd></dl>

Arguments

<dl>
<dt><code>target</code></dt>
<dd>(<code>proportion</code>) target toxicity probability, except 0 or 1.</dd></dl>
<dt><code>asymmetry</code></dt>
<dd>(<code>number</code>) value of the asymmetry exponent in the
divergence function that describes the rate of penalization for overly
toxic does. It must be a value from \((0, 2)\) interval.</dd>

Slots

<a href='https://lifecycle.r-lib.org/articles/stages.html#stable'><img src='figures/lifecycle-stable.svg' alt='[Stable]' /></a>
<code>NextBestInfTheory</code> is the class for next best dose that is based on
information theory as proposed in https://doi.org/10.1002/sim.8450.

NextBestInfTheory — NextBestInfTheory-class

<dl>

<dt>target</dt>
<dd>(<code>proportion</code>) see slot definition.</dd>


<dt>asymmetry</dt>
<dd>(<code>number</code>) see slot definition.</dd>

</dl>

<dl>
<dt><code>target</code></dt>
<dd>(<code>proportion</code>) target toxicity probability, except 0 or 1.</dd>


<dt><code>asymmetry</code></dt>
<dd>(<code>number</code>) value of the asymmetry exponent in the
divergence function that describes the rate of penalization for overly
toxic does. It must be a value from \((0, 2)\) interval.</dd>


</dl>

<code>NextBestInfTheory</code>

NextBestInfTheory-class: `NextBestInfTheory`

Description

Usage

Arguments

Slots