h_model_dual_endpoint_sigma2betaw

<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>
A simple helper function that takes <code>DualEndpoint</code> object and updates
<code>priormodel</code>, <code>modelspecs</code>, <code>init</code>, <code>sample</code> slots according to the random walk
variance.

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 

h_model_dual_endpoint_sigma2betaw function

<dl><dt>use_fixed</dt>
<dd>(<code>flag</code>) indicates whether a fixed value for
<code>sigma2betaW</code> should be used or not. If <code>sigma2betaW</code> is not supposed
to be a fixed value, a prior distribution from the Inverse-Gamma distribution
will be used. See the details below, under <code>sigma2betaW</code> argument.</dd>
<dt>sigma2betaW</dt>
<dd>(<code>numeric</code>) the prior variance factor of the random walk
prior for the biomarker model. Either a fixed value or Inverse-Gamma distribution
parameters, i.e. vector with two elements named <code>a</code> and <code>b</code>.</dd>
<dt>de</dt>
<dd>(<code>DualEnpoint</code>) dual endpoint model whose slots will be updated.</dd></dl>

Arguments

<a href='https://lifecycle.r-lib.org/articles/stages.html#stable'><img src='figures/lifecycle-stable.svg' alt='[Stable]' /></a>
A simple helper function that takes <code>DualEndpoint</code> object and updates
<code>priormodel</code>, <code>modelspecs</code>, <code>init</code>, <code>sample</code> slots according to the random walk
variance.

Update certain components of DualEndpoint model with regard to prior variance
factor of the random walk. — h_model_dual_endpoint_sigma2betaw

<dl>

<dt>use_fixed</dt>
<dd>(<code>flag</code>) indicates whether a fixed value for
<code>sigma2betaW</code> should be used or not. If <code>sigma2betaW</code> is not supposed
to be a fixed value, a prior distribution from the Inverse-Gamma distribution
will be used. See the details below, under <code>sigma2betaW</code> argument.</dd>


<dt>sigma2betaW</dt>
<dd>(<code>numeric</code>) the prior variance factor of the random walk
prior for the biomarker model. Either a fixed value or Inverse-Gamma distribution
parameters, i.e. vector with two elements named <code>a</code> and <code>b</code>.</dd>


<dt>de</dt>
<dd>(<code>DualEnpoint</code>) dual endpoint model whose slots will be updated.</dd>

</dl>

Update certain components of <code>DualEndpoint</code> model with regard to prior variance
factor of the random walk.

h_model_dual_endpoint_sigma2betaw: Update certain components of `DualEndpoint` model with regard to prior variance factor of the random walk.

Description

Usage

Value

Arguments

See Also