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

Retrieve Propensity for Tx Received

<table summary="R argblock">
<tr valign="top">
<td><code>propenObj</code></td>
<td>
a PropensityObj
</td>
</tr>
<tr valign="top">
<td><code>txObj</code></td>
<td>
a TxObj
</td>
</tr>
<tr valign="top">
<td><code>data</code></td>
<td>
a data.frame
</td>
</tr>
</table>

vector of propensity for tx received.

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

getPrWgt

Description

Usage

Arguments

Value

DynTxRegime

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`propenObj`	a PropensityObj
`txObj`	a TxObj
`data`	a data.frame