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>Class <code>OutcomeObj</code> groups outcome regression results under a common
name
</p>

<dl>
<dt><code>outcome</code></dt>
<dd>
<p>ANY - expected to be <code>OutcomeNoFit</code>, 
<code>OutcomeSimpleFit</code>,
<code>OutcomeSimpleFit_fSet</code>, <code>OutcomeSimpleFit_SubsetList</code>, 
<code>OutcomeIterateFit</code>, or <code>DecisionPointList</code>.</p>
</dd>
</dl>

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

OutcomeObj-class

Description

Slots

DynTxRegime

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