myBayesLogit

Do MCMC sampling for Bayesian logistic regression model

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

myBayesLogit function

<dl><dt>y</dt>
<dd>0/1 vector of responses</dd>
<dt>X</dt>
<dd>design matrix</dd>
<dt>m0</dt>
<dd>prior mean vector</dd>
<dt>P0</dt>
<dd>precision matrix</dd>
<dt>options</dt>
<dd>McmcOptions object</dd></dl>

Arguments

Do MCMC sampling for Bayesian logistic regression model — myBayesLogit

<dl>

<dt>y</dt>
<dd>0/1 vector of responses</dd>


<dt>X</dt>
<dd>design matrix</dd>


<dt>m0</dt>
<dd>prior mean vector</dd>


<dt>P0</dt>
<dd>precision matrix</dd>


<dt>options</dt>
<dd>McmcOptions object</dd>

</dl>

myBayesLogit: Do MCMC sampling for Bayesian logistic regression model

Description

Usage

Value

Arguments