OneParExpPrior-class

.OneParExpPrior

OneParExpPrior

.DefaultOneParExpPrior

<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>OneParExpPrior</code> is the class for a standard CRM with an exponential prior
on the power parameter for the skeleton prior probabilities. It is an
implementation of a version of the one-parameter CRM
OQuigleyPepeFisher1990crmPack.

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 

OneParExpPrior-class function

<dl><dt>skel_probs</dt>
<dd>see slot definition.</dd>
<dt>dose_grid</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>lambda</dt>
<dd>see slot definition.</dd></dl>

Arguments

<dl>
<dt><code>skel_fun</code></dt>
<dd>(<code>function</code>) function to calculate the prior DLT probabilities.</dd></dl>
<dt><code>skel_fun_inv</code></dt>
<dd>(<code>function</code>) inverse function of <code>skel_fun</code>.</dd>
<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>lambda</code></dt>
<dd>(<code>number</code>) rate parameter of prior exponential distribution
for theta.</dd>

Slots

<a href='https://lifecycle.r-lib.org/articles/stages.html#experimental'><img src='figures/lifecycle-experimental.svg' alt='[Experimental]' /></a>
<code>OneParExpPrior</code> is the class for a standard CRM with an exponential prior
on the power parameter for the skeleton prior probabilities. It is an
implementation of a version of the one-parameter CRM
OQuigleyPepeFisher1990crmPack.

OneParExpPrior — OneParExpPrior-class

<dl>

<dt>skel_probs</dt>
<dd>see slot definition.</dd>


<dt>dose_grid</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>lambda</dt>
<dd>see slot definition.</dd>

</dl>

<dl>
<dt><code>skel_fun</code></dt>
<dd>(<code>function</code>) function to calculate the prior DLT probabilities.</dd>


<dt><code>skel_fun_inv</code></dt>
<dd>(<code>function</code>) inverse function of <code>skel_fun</code>.</dd>


<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>lambda</code></dt>
<dd>(<code>number</code>) rate parameter of prior exponential distribution
for theta.</dd>


</dl>

<code>OneParExpPrior</code>

OneParExpPrior-class: `OneParExpPrior`

Description

Usage

Arguments

Slots

References

Examples