h_group_data

This is only used in the simulation method for <code>DesignGrouped</code> to combine
the separately generated data sets from mono and combo arms and to fit the
combined logistic regression model.
Hence the ID and cohort information is not relevant and will be
arbitrarily assigned to avoid problems with the <code>DataGrouped</code> validation.

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 

h_group_data function

<dl><dt>mono_data</dt>
<dd>(<code>Data</code>) mono data.</dd>
<dt>combo_data</dt>
<dd>(<code>Data</code>) combo data.</dd></dl>

Arguments

Group Together Mono and Combo Data — h_group_data

<dl>

<dt>mono_data</dt>
<dd>(<code>Data</code>) mono data.</dd>


<dt>combo_data</dt>
<dd>(<code>Data</code>) combo data.</dd>

</dl>

Group Together Mono and Combo Data

h_group_data: Group Together Mono and Combo Data

Description

Usage

Value

Arguments