LogisticNormalMixture

Initialization function for the "LogisticNormalMixture" class

methods

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

LogisticNormalMixture function

<dl><dt>comp1</dt>
<dd>the specifications of the first component: a list with
<code>mean</code> and <code>cov</code> for the first bivariate normal prior</dd>
<dt>comp2</dt>
<dd>the specifications of the second component</dd>
<dt>weightpar</dt>
<dd>the beta parameters for the weight of the first component</dd>
<dt>refDose</dt>
<dd>the reference dose</dd></dl>

Arguments

Initialization function for the "LogisticNormalMixture" class — LogisticNormalMixture

<dl>

<dt>comp1</dt>
<dd>the specifications of the first component: a list with
<code>mean</code> and <code>cov</code> for the first bivariate normal prior</dd>


<dt>comp2</dt>
<dd>the specifications of the second component</dd>


<dt>weightpar</dt>
<dd>the beta parameters for the weight of the first component</dd>


<dt>refDose</dt>
<dd>the reference dose</dd>

</dl>

LogisticNormalMixture: Initialization function for the "LogisticNormalMixture" class

Description

Usage

Value

Arguments