estimate_marginal

<code>estimate_marginal()</code> estimates the marginal distribution specified by
<code>marginal_Y</code> using maximum likelihood. The optimizer is Newton-Raphson.

In a clinical trial, it frequently occurs that the most credible
outcome to evaluate the effectiveness of a new therapy (the true endpoint) is
difficult to measure. In such a situation, it can be an effective strategy to
replace the true endpoint by a (bio)marker that is easier to measure and that
allows for a prediction of the treatment effect on the true endpoint (a
surrogate endpoint). The package 'Surrogate' allows for an evaluation of the
appropriateness of a candidate surrogate endpoint based on the meta-analytic,
information-theoretic, and causal-inference frameworks. Part of this software
has been developed using funding provided from the European Union's Seventh
Framework Programme for research, technological development and demonstration
(Grant Agreement no 602552), the Special Research Fund (BOF) of Hasselt
University (BOF-number: BOF2OCPO3), GlaxoSmithKline Biologicals, Baekeland
Mandaat (HBC.2022.0145), and Johnson & Johnson Innovative Medicine.

Wim der Elst

Surrogate

Evaluation of Surrogate Endpoints in Clinical Trials

Wim Van Der Elst

Florian Stijven

Fenny Ong

Dries De Witte

Paul Meyvisch

Alvaro Poveda

Ariel Alonso

Hannah Ensor

Christoper Weir

Geert Molenberghs

estimate_marginal function

<dl><dt>Y</dt>
<dd>Observations (continuous)</dd>
<dt>marginal_Y</dt>
<dd>List with the following five elements (in order):<ul>
<li>Density function with first argument <code>x</code> and second argument <code>para</code> the parameter
vector for this distribution.</li>
<li>Distribution function with first argument <code>x</code> and second argument <code>para</code>.</li>
<li>Inverse distribution function with first argument <code>p</code> and second argument <code>para</code>.</li>
<li>The number of elements in <code>para</code>.</li>
<li>Starting values for <code>para</code>.</li>
</ul></dd>
<dt>starting_values</dt>
<dd>Starting values for <code>marginal_Y</code></dd></dl>

Arguments

Estimate marginal distribution using ML — estimate_marginal

<dl>

<dt>Y</dt>
<dd>Observations (continuous)</dd>


<dt>marginal_Y</dt>
<dd>List with the following five elements (in order):<ul>
<li>Density function with first argument <code>x</code> and second argument <code>para</code> the parameter
vector for this distribution.</li>
<li>Distribution function with first argument <code>x</code> and second argument <code>para</code>.</li>
<li>Inverse distribution function with first argument <code>p</code> and second argument <code>para</code>.</li>
<li>The number of elements in <code>para</code>.</li>
<li>Starting values for <code>para</code>.</li>
</ul></dd>


<dt>starting_values</dt>
<dd>Starting values for <code>marginal_Y</code></dd>

</dl>

Estimate marginal distribution using ML

estimate_marginal: Estimate marginal distribution using ML

Description

Usage

Value

Arguments