NextBestProbMTDMinDist-class

.NextBestProbMTDMinDist

NextBestProbMTDMinDist

.DefaultNextBestProbMTDMinDist

<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>NextBestProbMTDMinDist</code> is the class of finding a next best dose that selects
the dose with the highest probability of having a toxicity rate with the
smallest distance to the toxicity target.
The dose is determined by calculating the posterior toxicity probability
for each dose per iteration and select the dose that has the smallest toxicity
probability distance to the target. The dose with the highest frequency
of being selected as MTD across iterations is the next best dose. Placebo
is not considered as the next dose and for that reason not used in
calculations. I.e. for placebo the toxicity probability distance to target
is not calculated and taken into account for determination of the next dose.

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 

NextBestProbMTDMinDist-class function

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

Arguments

<dl>
<dt><code>target</code></dt>
<dd>(<code>numeric</code>) the target toxicity probability.</dd></dl>

Slots

<a href='https://lifecycle.r-lib.org/articles/stages.html#experimental'><img src='figures/lifecycle-experimental.svg' alt='[Experimental]' /></a>
<code>NextBestProbMTDMinDist</code> is the class of finding a next best dose that selects
the dose with the highest probability of having a toxicity rate with the
smallest distance to the toxicity target.
The dose is determined by calculating the posterior toxicity probability
for each dose per iteration and select the dose that has the smallest toxicity
probability distance to the target. The dose with the highest frequency
of being selected as MTD across iterations is the next best dose. Placebo
is not considered as the next dose and for that reason not used in
calculations. I.e. for placebo the toxicity probability distance to target
is not calculated and taken into account for determination of the next dose.

NextBestProbMTDMinDist — NextBestProbMTDMinDist-class

<dl>

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

</dl>

<dl>
<dt><code>target</code></dt>
<dd>(<code>numeric</code>) the target toxicity probability.</dd>


</dl>

<code>NextBestProbMTDMinDist</code>

NextBestProbMTDMinDist-class: `NextBestProbMTDMinDist`

Description

Usage

Arguments

Slots

Examples