h_info_theory_dist

<a href="https://lifecycle.r-lib.org/articles/stages.html#experimental"><img src="figures/lifecycle-experimental.svg?package=crmPack&version=2.0.0" alt="[Experimental]"></a>
Helper function which provides the value of the
divergence as given by equation in (7) in the reference at
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 

h_info_theory_dist function

<dl><dt>prob</dt>
<dd>(<code>numeric</code>) vector or matrix with probabilities of a DLT occurring.</dd>
<dt>target</dt>
<dd>(<code>number </code>) single target probability of a DLT.</dd>
<dt>asymmetry</dt>
<dd>(<code>number</code>) describes the rate of penalization
for overly toxic does, range 0 to 2.</dd></dl>

Arguments

<a href='https://lifecycle.r-lib.org/articles/stages.html#experimental'><img src='figures/lifecycle-experimental.svg' alt='[Experimental]' /></a>
Helper function which provides the value of the
divergence as given by equation in (7) in the reference at
https://doi.org/10.1002/sim.8450.

Calculating the Information Theoretic Distance — h_info_theory_dist

<dl>

<dt>prob</dt>
<dd>(<code>numeric</code>) vector or matrix with probabilities of a DLT occurring.</dd>


<dt>target</dt>
<dd>(<code>number </code>) single target probability of a DLT.</dd>


<dt>asymmetry</dt>
<dd>(<code>number</code>) describes the rate of penalization
for overly toxic does, range 0 to 2.</dd>

</dl>

Calculating the Information Theoretic Distance

h_info_theory_dist: Calculating the Information Theoretic Distance

Description

Usage

Arguments

Examples