binary_continuous_loglik

Loglikelihood function for binary-continuous copula model

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

binary_continuous_loglik function

<dl><dt>para</dt>
<dd>Parameter vector. The parameters are ordered as follows:<ul>
<li><code>para[1]</code>: mean parameter for latent true endpoint distribution</li>
<li><code>para[2:p]</code>: Parameters for surrogate distribution, more details in
<code>?Surrogate::cdf_fun</code> for the specific implementations.</li>
<li><code>para[p + 1]</code>: copula parameter</li>
</ul></dd>
<dt>X</dt>
<dd>First variable (continuous)</dd>
<dt>Y</dt>
<dd>Second variable (binary, $0$ or $1$)</dd>
<dt>copula_family</dt>
<dd>Copula family, one of the following:<ul>
<li><code>"clayton"</code></li>
<li><code>"frank"</code></li>
<li><code>"gumbel"</code></li>
<li><code>"gaussian"</code></li>
</ul></dd>
<dt>marginal_surrogate</dt>
<dd>Marginal distribution for the surrogate. For all
available options, see <code>?Surrogate::cdf_fun</code>.</dd></dl>

Arguments

Loglikelihood function for binary-continuous copula model — binary_continuous_loglik

<dl>

<dt>para</dt>
<dd>Parameter vector. The parameters are ordered as follows:<ul>
<li><code>para[1]</code>: mean parameter for latent true endpoint distribution</li>
<li><code>para[2:p]</code>: Parameters for surrogate distribution, more details in
<code>?Surrogate::cdf_fun</code> for the specific implementations.</li>
<li><code>para[p + 1]</code>: copula parameter</li>
</ul></dd>


<dt>X</dt>
<dd>First variable (continuous)</dd>


<dt>Y</dt>
<dd>Second variable (binary, $0$ or $1$)</dd>


<dt>copula_family</dt>
<dd>Copula family, one of the following:<ul>
<li><code>"clayton"</code></li>
<li><code>"frank"</code></li>
<li><code>"gumbel"</code></li>
<li><code>"gaussian"</code></li>
</ul></dd>


<dt>marginal_surrogate</dt>
<dd>Marginal distribution for the surrogate. For all
available options, see <code>?Surrogate::cdf_fun</code>.</dd>

</dl>

Loglikelihood function for binary-continuous copula model

binary_continuous_loglik: Loglikelihood function for binary-continuous copula model

Description

Usage

Value

Arguments