FractionalCRM-class

.FractionalCRM

FractionalCRM

.DefaultFractionalCRM

<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>FractionalCRM</code> is the class for a fractional CRM model based on a one
parameter CRM (with normal prior on the log-power parameter) as well as
Kaplan-Meier based estimation of the conditional probability to experience a
DLT for non-complete observations.
This fractional CRM model follows the paper and code by YinZhengXu2013;textualcrmPack.

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 

FractionalCRM-class function

<dl><dt>...</dt>
<dd>Arguments passed on to <code>OneParLogNormalPrior</code><dl>
 <dt><code>skel_probs</code></dt>
<dd>(<code>numeric</code>) skeleton prior probabilities. This is a vector
of unique and sorted probability values between 0 and 1.</dd></dl></dd> <dt><code>dose_grid</code></dt>
<dd>(<code>numeric</code>) dose grid. It must be must be a sorted vector
of the same length as <code>skel_probs</code>.</dd> <dt><code>sigma2</code></dt>
<dd>(<code>number</code>) prior variance of log power parameter alpha.</dd> 
</dl>

Arguments

<a href='https://lifecycle.r-lib.org/articles/stages.html#stable'><img src='figures/lifecycle-stable.svg' alt='[Stable]' /></a>
<code>FractionalCRM</code> is the class for a fractional CRM model based on a one
parameter CRM (with normal prior on the log-power parameter) as well as
Kaplan-Meier based estimation of the conditional probability to experience a
DLT for non-complete observations.
This fractional CRM model follows the paper and code by YinZhengXu2013;textualcrmPack.

FractionalCRM — FractionalCRM-class

<dl>

<dt>...</dt>
<dd>Arguments passed on to <code>OneParLogNormalPrior</code><dl>
 <dt><code>skel_probs</code></dt>
<dd>(<code>numeric</code>) skeleton prior probabilities. This is a vector
of unique and sorted probability values between 0 and 1.</dd>

 <dt><code>dose_grid</code></dt>
<dd>(<code>numeric</code>) dose grid. It must be must be a sorted vector
of the same length as <code>skel_probs</code>.</dd>

 <dt><code>sigma2</code></dt>
<dd>(<code>number</code>) prior variance of log power parameter alpha.</dd>

 
</dl></dd>

</dl>

<code>FractionalCRM</code>

FractionalCRM-class: `FractionalCRM`

Description

Usage

Arguments

References

See Also

Examples