c_model

c_cox

c_no_censoring

Provides constructors for right-censoring models.
Main constructors include:<ul>
<li><code>c_cox()</code>: Cox proportional hazards model</li>
<li><code>c_no_censoring()</code>: Model for scenarios without censoring
The constructors are used as input for <code>policy_eval()</code> and <code>policy_learn()</code>.</li>
</ul>

Package for learning and evaluating (subgroup) policies via doubly robust loss functions. Policy learning methods include doubly robust blip/conditional average treatment effect learning and sequential policy tree learning. Methods for (subgroup) policy evaluation include doubly robust cross-fitting and online estimation/sequential validation. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.

Andreas Nordland

polle

Policy Learning

Klaus Holst

c_model function

<dl><dt>formula</dt>
<dd>An object of class <a href="/link/formula?package=polle&version=1.6.2" data-mini-rdoc="polle::formula">formula</a> specifying the design matrix for
the right-censoring model. Use <code>get_history_names()</code> to view the available
variable names.</dd>
<dt>offset</dt>
<dd>offsets for Cox model, see <code>mets::phreg()</code></dd>
<dt>weights</dt>
<dd>weights for Cox score equations, see <code>mets::phreg()</code></dd>
<dt>...</dt>
<dd>Additional arguments passed to the model.</dd></dl>

Arguments

c_model class object — c_model

<dl>

<dt>formula</dt>
<dd>An object of class <a href='https://rdrr.io/r/stats/formula.html'>formula</a> specifying the design matrix for
the right-censoring model. Use <code>get_history_names()</code> to view the available
variable names.</dd>


<dt>offset</dt>
<dd>offsets for Cox model, see <code>mets::phreg()</code></dd>


<dt>weights</dt>
<dd>weights for Cox score equations, see <code>mets::phreg()</code></dd>


<dt>...</dt>
<dd>Additional arguments passed to the model.</dd>

</dl>

c_model: c_model class object

Description

Usage

Value

Arguments

Details

See Also