myBayesLogit

<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>
Replacement for <code>BayesLogit::logit</code>. Performs MCMC sampling for Bayesian
logistic regression using JAGS.

internal

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 

myBayesLogit function

<dl><dt>y</dt>
<dd>(<code>integer</code>) 0/1 vector of responses.</dd>
<dt>X</dt>
<dd>(<code>matrix</code>) design matrix.</dd>
<dt>m0</dt>
<dd>(<code>numeric</code>) prior mean vector.</dd>
<dt>P0</dt>
<dd>(<code>matrix</code>) precision matrix.</dd>
<dt>options</dt>
<dd>(<code>McmcOptions</code>) MCMC options.</dd></dl>

Arguments

<a href='https://lifecycle.r-lib.org/articles/stages.html#stable'><img src='figures/lifecycle-stable.svg' alt='[Stable]' /></a>
Replacement for <code>BayesLogit::logit</code>. Performs MCMC sampling for Bayesian
logistic regression using JAGS.

MCMC Sampling for Bayesian Logistic Regression Model — myBayesLogit

<dl>

<dt>y</dt>
<dd>(<code>integer</code>) 0/1 vector of responses.</dd>


<dt>X</dt>
<dd>(<code>matrix</code>) design matrix.</dd>


<dt>m0</dt>
<dd>(<code>numeric</code>) prior mean vector.</dd>


<dt>P0</dt>
<dd>(<code>matrix</code>) precision matrix.</dd>


<dt>options</dt>
<dd>(<code>McmcOptions</code>) MCMC options.</dd>

</dl>

MCMC Sampling for Bayesian Logistic Regression Model

myBayesLogit: MCMC Sampling for Bayesian Logistic Regression Model

Description

Usage

Value

Arguments