StoppingMTDCV-class

.StoppingMTDCV

StoppingMTDCV

.DefaultStoppingMTDCV

<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>
<code>StoppingMTDCV</code> is a class for stopping rule based on precision of MTD
which is calculated as the coefficient of variation (CV) of the MTD.
Here, the MTD is defined as the dose that reaches a specific <code>target</code>
probability of the occurrence of a DLT.

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 

StoppingMTDCV-class function

<dl><dt>target</dt>
<dd>(<code>proportion</code>) see slot definition.</dd>
<dt>thresh_cv</dt>
<dd>(<code>number</code>) see slot definition.</dd>
<dt>report_label</dt>
<dd>(<code>string</code> or <code>NA</code>) see slot definition.</dd></dl>

Arguments

<dl>
<dt><code>target</code></dt>
<dd>(<code>proportion</code>) toxicity target of MTD (except 0 or 1).</dd></dl>
<dt><code>thresh_cv</code></dt>
<dd>(<code>number</code>) threshold (percentage &gt; 0) for CV to be
considered accurate enough to stop the trial. The stopping occurs when the
CV is less than or equal to <code>tresh_cv</code>.</dd>

Slots

<a href='https://lifecycle.r-lib.org/articles/stages.html#experimental'><img src='figures/lifecycle-experimental.svg' alt='[Experimental]' /></a>
<code>StoppingMTDCV</code> is a class for stopping rule based on precision of MTD
which is calculated as the coefficient of variation (CV) of the MTD.
Here, the MTD is defined as the dose that reaches a specific <code>target</code>
probability of the occurrence of a DLT.

StoppingMTDCV — StoppingMTDCV-class

<dl>

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


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


<dt>report_label</dt>
<dd>(<code>string</code> or <code>NA</code>) see slot definition.</dd>

</dl>

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


<dt><code>thresh_cv</code></dt>
<dd>(<code>number</code>) threshold (percentage &gt; 0) for CV to be
considered accurate enough to stop the trial. The stopping occurs when the
CV is less than or equal to <code>tresh_cv</code>.</dd>


</dl>

<code>StoppingMTDCV</code>

StoppingMTDCV-class: `StoppingMTDCV`

Description

Usage

Arguments

Slots

Examples