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controlling for a user-specified set of baseline covariates." />
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<h1>TMLE procedure using ATE for Biomarker Identication from Exposure</h1>
<small class="dont-index">Source: <a href='https://github.com/nhejazi/biotmle/blob/master/R/biotmle.R'><code>R/biotmle.R</code></a></small>
<div class="hidden name"><code>exp_biomarkertmle.Rd</code></div>
</div>
<div class="ref-description">
<p>This function performs influence curve-based estimation of the effect of an
exposure on biological expression values associated with a given biomarker,
controlling for a user-specified set of baseline covariates.</p>
</div>
<pre class="usage"><span class='fu'>exp_biomarkertmle</span><span class='op'>(</span><span class='va'>Y</span>, <span class='va'>A</span>, <span class='va'>W</span>, <span class='va'>g_lib</span>, <span class='va'>Q_lib</span>, <span class='va'>cv_folds</span>, <span class='va'>...</span><span class='op'>)</span></pre>
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>Y</th>
<td><p>A <code>numeric</code> vector of expression values for a given biomarker.</p></td>
</tr>
<tr>
<th>A</th>
<td><p>A <code>numeric</code> vector of discretized exposure vector (e.g., from
a design matrix whose effect on expression values is of interest.</p></td>
</tr>
<tr>
<th>W</th>
<td><p>A <code>Matrix</code> of <code>numeric</code> values corresponding to baseline
covariates to be marginalized over in the estimation process.</p></td>
</tr>
<tr>
<th>g_lib</th>
<td><p>A <code>character</code> vector identifying the library of learning
algorithms to be used in fitting the propensity score P[A = a | W].</p></td>
</tr>
<tr>
<th>Q_lib</th>
<td><p>A <code>character</code> vector identifying the library of learning
algorithms to be used in fitting the outcome regression E[Y | A, W].</p></td>
</tr>
<tr>
<th>cv_folds</th>
<td><p>A <code>numeric</code> scalar indicating how many folds to use in
performing targeted minimum loss estimation. Cross-validated estimates are
more robust, allowing relaxing of theoretical conditions and construction
of conservative variance estimates.</p></td>
</tr>
<tr>
<th>...</th>
<td><p>Additional arguments passed to <code><a href='https://rdrr.io/pkg/drtmle/man/drtmle.html'>drtmle</a></code> in
computing the targeted minimum loss estimator of the average treatment
effect.</p></td>
</tr>
</table>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>TMLE-based estimate of the relationship between biomarker expression
and changes in an exposure variable, computed iteratively and saved in the
<code>tmleOut</code> slot in a <code>biotmle</code> object.</p>
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