h_next_best_mg_doses_at_grid

<a href="https://lifecycle.r-lib.org/articles/stages.html#experimental"><img src="figures/lifecycle-experimental.svg?package=crmPack&version=2.0.0" alt="[Experimental]"></a>
Helper function that for a given target doses finds the dose in grid that is
closest and below the target. There are four different targets in the context
of <code>nextBest-NextBestMaxGain()</code> method: \(min(`dose_mg`, `dose_target_drt`)\),
<code>dose_mg</code>, <code>dose_target_drt</code> or <code>dose_target_eot</code>.

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_next_best_mg_doses_at_grid function

<dl><dt>dose_target_drt</dt>
<dd>(<code>number</code>) target dose estimate during the trial.</dd>
<dt>dose_target_eot</dt>
<dd>(<code>number</code>) target dose estimate at the end of the trial.</dd>
<dt>dose_mg</dt>
<dd>(<code>number</code>) the dose corresponding to the maximum gain.</dd>
<dt>dose_grid</dt>
<dd>(<code>numeric</code>) all possible doses.</dd>
<dt>doselimit</dt>
<dd>(<code>number</code>) the maximum allowed next dose.</dd>
<dt>placebo</dt>
<dd>(<code>flag</code>) if <code>TRUE</code> the first dose level in the <code>dose_grid</code>
is considered as placebo.</dd></dl>

Arguments

<a href='https://lifecycle.r-lib.org/articles/stages.html#experimental'><img src='figures/lifecycle-experimental.svg' alt='[Experimental]' /></a>
Helper function that for a given target doses finds the dose in grid that is
closest and below the target. There are four different targets in the context
of <code>nextBest-NextBestMaxGain()</code> method: \(min(`dose_mg`, `dose_target_drt`)\),
<code>dose_mg</code>, <code>dose_target_drt</code> or <code>dose_target_eot</code>.

Get Closest Grid Doses for a Given Target Doses for nextBest-NextBestMaxGain Method. — h_next_best_mg_doses_at_grid

<dl>

<dt>dose_target_drt</dt>
<dd>(<code>number</code>) target dose estimate during the trial.</dd>


<dt>dose_target_eot</dt>
<dd>(<code>number</code>) target dose estimate at the end of the trial.</dd>


<dt>dose_mg</dt>
<dd>(<code>number</code>) the dose corresponding to the maximum gain.</dd>


<dt>dose_grid</dt>
<dd>(<code>numeric</code>) all possible doses.</dd>


<dt>doselimit</dt>
<dd>(<code>number</code>) the maximum allowed next dose.</dd>


<dt>placebo</dt>
<dd>(<code>flag</code>) if <code>TRUE</code> the first dose level in the <code>dose_grid</code>
is considered as placebo.</dd>

</dl>

Get Closest Grid Doses for a Given Target Doses for <code>nextBest-NextBestMaxGain</code> Method.

h_next_best_mg_doses_at_grid: Get Closest Grid Doses for a Given Target Doses for `nextBest-NextBestMaxGain` Method.

Description

Usage

Arguments