Methods to estimate dynamic treatment regimes using Interactive
Q-Learning, Q-Learning, weighted learning, and value-search methods based on
Augmented Inverse Probability Weighted Estimators and Inverse Probability
Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for
Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B.,
Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.

Methods for Estimating Optimal Dynamic Treatment Regimes

package-ubuntu-20.04-r-4.0.5#cran#DynTxRegime-4.10

<p>Calls newRegime and stores object in @regime.
</p>

.newRegimeObj(object)

## S4 method for signature ''function''
.newRegimeObj(object)

## S4 method for signature 'list'
.newRegimeObj(object)

<table summary="R argblock"><tr valign="top">
<td><code>object</code></td>
<td>
<p>A function defining the treatment regime</p>
</td>
</tr></table>

package-ubuntu-20.04-r-4.0.5#cran#DynTxRegime-4.10#docs#newRegimeObj

newRegimeObj

Description

Usage

Arguments

DynTxRegime

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