NextBestMaxGain-class

.NextBestMaxGain

NextBestMaxGain

.DefaultNextBestMaxGain

<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>NextBestMaxGain</code> is the class to find a next best dose with maximum gain
value based on a pseudo DLT and efficacy models without samples. It is based
solely on the probabilities of the occurrence of a DLT and the values
of the mean efficacy responses obtained by using the modal estimates of the
DLT and efficacy model parameters. There are two target probabilities of the
occurrence of a DLT that must be specified: target probability to be used
during the trial and target probability to be used at the end of the trial.
It is suitable to use it only with the <code>ModelTox</code> model and <code>ModelEff</code>
classes (except <code>EffFlexi</code>).

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 

NextBestMaxGain-class function

<dl><dt>prob_target_drt</dt>
<dd>(<code>proportion</code>) see slot definition.</dd>
<dt>prob_target_eot</dt>
<dd>(<code>proportion</code>) see slot definition.</dd></dl>

Arguments

<dl>
<dt><code>prob_target_drt</code></dt>
<dd>(<code>proportion</code>) the target probability of the
occurrence of a DLT to be used during the trial.</dd></dl>
<dt><code>prob_target_eot</code></dt>
<dd>(<code>proportion</code>) the target probability of the
occurrence of a DLT to be used at the end of the trial.</dd>

Slots

<a href='https://lifecycle.r-lib.org/articles/stages.html#stable'><img src='figures/lifecycle-stable.svg' alt='[Stable]' /></a>
<code>NextBestMaxGain</code> is the class to find a next best dose with maximum gain
value based on a pseudo DLT and efficacy models without samples. It is based
solely on the probabilities of the occurrence of a DLT and the values
of the mean efficacy responses obtained by using the modal estimates of the
DLT and efficacy model parameters. There are two target probabilities of the
occurrence of a DLT that must be specified: target probability to be used
during the trial and target probability to be used at the end of the trial.
It is suitable to use it only with the <code>ModelTox</code> model and <code>ModelEff</code>
classes (except <code>EffFlexi</code>).

NextBestMaxGain — NextBestMaxGain-class

<dl>

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


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

</dl>

<dl>
<dt><code>prob_target_drt</code></dt>
<dd>(<code>proportion</code>) the target probability of the
occurrence of a DLT to be used during the trial.</dd>


<dt><code>prob_target_eot</code></dt>
<dd>(<code>proportion</code>) the target probability of the
occurrence of a DLT to be used at the end of the trial.</dd>


</dl>

<code>NextBestMaxGain</code>

NextBestMaxGain-class: `NextBestMaxGain`

Description

Usage

Arguments

Slots

Examples