DualEndpointOld-class

classes

internal

Implements a wide range of model-based dose
escalation 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. The
focus is on Bayesian inference, making it very easy to setup a new design
with its own 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.

Daniel Sabanes Bove

crmPack

Object-Oriented Implementation of CRM Designs

Wai Yin Yeung

Giuseppe Palermo

Thomas Jaki

DualEndpointOld-class function

<dl>
<dt><code>mu</code></dt>
<dd>For the probit toxicity model, <code>mu</code> contains the prior mean
vector</dd></dl>
<dt><code>Sigma</code></dt>
<dd>For the probit toxicity model, contains the prior covariance
matrix</dd>
<dt><code>sigma2betaW</code></dt>
<dd>For the biomarker model, contains the prior variance
factor of the random walk prior. If it is not a single number, it can also
contain a vector with elements <code>a</code> and <code>b</code> for the inverse-gamma prior
on <code>sigma2betaW</code>.</dd>
<dt><code>sigma2W</code></dt>
<dd>Either a fixed value for the biomarker variance, or a vector
with elements <code>a</code> and <code>b</code> for the inverse-gamma prior parameters.</dd>
<dt><code>rho</code></dt>
<dd>Either a fixed value for the correlation (between -1 and 1), or a
vector with elements <code>a</code> and <code>b</code> for the Beta prior on the
transformation kappa = (rho + 1) / 2, which is in (0, 1). For example,
<code>a=1,b=1</code> leads to a uniform prior on rho.</dd>
<dt><code>useRW1</code></dt>
<dd>for specifying the random walk prior on the biomarker level: if
<code>TRUE</code>, RW1 is used, otherwise RW2.</dd>
<dt><code>useFixed</code></dt>
<dd>a list with logical value for each of the three parameters
<code>sigma2betaW</code>, <code>sigma2W</code> and <code>rho</code> indicating whether
a fixed value is used or not.</dd>

Slots

Dual endpoint model — DualEndpointOld-class

<dl>
<dt><code>mu</code></dt>
<dd>For the probit toxicity model, <code>mu</code> contains the prior mean
vector</dd>


<dt><code>Sigma</code></dt>
<dd>For the probit toxicity model, contains the prior covariance
matrix</dd>


<dt><code>sigma2betaW</code></dt>
<dd>For the biomarker model, contains the prior variance
factor of the random walk prior. If it is not a single number, it can also
contain a vector with elements <code>a</code> and <code>b</code> for the inverse-gamma prior
on <code>sigma2betaW</code>.</dd>


<dt><code>sigma2W</code></dt>
<dd>Either a fixed value for the biomarker variance, or a vector
with elements <code>a</code> and <code>b</code> for the inverse-gamma prior parameters.</dd>


<dt><code>rho</code></dt>
<dd>Either a fixed value for the correlation (between -1 and 1), or a
vector with elements <code>a</code> and <code>b</code> for the Beta prior on the
transformation kappa = (rho + 1) / 2, which is in (0, 1). For example,
<code>a=1,b=1</code> leads to a uniform prior on rho.</dd>


<dt><code>useRW1</code></dt>
<dd>for specifying the random walk prior on the biomarker level: if
<code>TRUE</code>, RW1 is used, otherwise RW2.</dd>


<dt><code>useFixed</code></dt>
<dd>a list with logical value for each of the three parameters
<code>sigma2betaW</code>, <code>sigma2W</code> and <code>rho</code> indicating whether
a fixed value is used or not.</dd>


</dl>

Dual endpoint model

DualEndpointOld-class: Dual endpoint model

Description

Arguments

Slots