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

Class <code>MethodObject</code> stores parameters required for optimization step

<dl>
<dt><code>x</code></dt>
<dd>
Matrix of covariates for kernel
</dd>
<dt><code>surrogate</code></dt>
<dd>
The Surrogate for the loss-function
</dd>
<dt><code>pars</code></dt>
<dd>
Vector of regime parameters
</dd>
<dt><code>kernel</code></dt>
<dd>
The Kernel defining the decision function
</dd>
</dl>

package-ubuntu-20.04-r-4.0.5#cran#DynTxRegime-4.10#docs#MethodObject-class

MethodObject-class

Description

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DynTxRegime

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