NextBestMaxGainSamples

please refer to <code><a href="/link/?package=crmPack&version=1.0.0" nextbestmaxgainsamples'="" data-mini-rdoc="crmPack::"></a>'>NextBestMaxGainSamples</code> class object

DLEDuringTrialtarget

DLEEndOfTrialtarget

TDderive

Gstarderive

Initialization function for class "NextBestMaxGainSamples"

methods

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.

Giuseppe Palermo

crmPack

Object-Oriented Implementation of CRM Designs

Daniel Sabanes Bove

Wai Yin Yeung

Thomas Jaki

NextBestMaxGainSamples function

please refer to <code><a href='<a href='NextBestMaxGainSamples'></a>'>NextBestMaxGainSamples</a></code> class object

NextBestMaxGainSamples: Initialization function for class "NextBestMaxGainSamples"

Description

Usage

Arguments

Value