fit_itr

Provides various statistical methods for evaluating
Individualized Treatment Rules under randomized data. The provided
metrics include Population Average Value (PAV), Population Average
Prescription Effect (PAPE), Area Under Prescription Effect Curve
(AUPEC). It also provides the tools to analyze Individualized
Treatment Rules under budget constraints. Detailed reference in Imai
and Li (2019) <arXiv:1905.05389>.

Michael Lingzhi Li

evalITR

Evaluating Individualized Treatment Rules

Kosuke Imai

Jialu Li

Xiaolong Yang

fit_itr function

<dl><dt>data</dt>
<dd>A dataset.</dd>
<dt>algorithms</dt>
<dd>Machine learning algorithms.</dd>
<dt>params</dt>
<dd>A list of parameters.</dd>
<dt>folds</dt>
<dd>Number of folds.</dd>
<dt>budget</dt>
<dd>The maximum percentage of population that can be treated under the budget constraint.</dd>
<dt>user_model</dt>
<dd>User's own function to estimated the ITR.</dd>
<dt>...</dt>
<dd>Additional arguments passed to <code><a href="/link/caret%3A%3Atrain?package=evalITR&version=1.0.0" data-mini-rdoc="evalITR::caret::train">caret::train</a></code></dd></dl>

Arguments

Estimate ITR for Single Outcome — fit_itr

<dl>

<dt>data</dt>
<dd>A dataset.</dd>


<dt>algorithms</dt>
<dd>Machine learning algorithms.</dd>


<dt>params</dt>
<dd>A list of parameters.</dd>


<dt>folds</dt>
<dd>Number of folds.</dd>


<dt>budget</dt>
<dd>The maximum percentage of population that can be treated under the budget constraint.</dd>


<dt>user_model</dt>
<dd>User's own function to estimated the ITR.</dd>


<dt>...</dt>
<dd>Additional arguments passed to <code><a href='https://rdrr.io/pkg/caret/man/train.html'>caret::train</a></code></dd>

</dl>

Last chance! 50% off unlimited learning

fit_itr: Estimate ITR for Single Outcome

Description

Usage

Value

Arguments