marginal_distribution

The <code>marginal_distribution()</code> function is a wrapper for
<code>fitdistrplus::fitdist()</code> that fits a univariate distribution to a data
vector.

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

Gokce Deliorman

Paul Meyvisch

Alvaro Poveda

Ariel Alonso

Hannah Ensor

Christoper Weir

Geert Molenberghs

marginal_distribution function

<dl><dt>x</dt>
<dd>(numeric) data vector</dd>
<dt>distribution</dt>
<dd>Distributional family. One of the follwing:<ul>
<li><code>"normal"</code>: normal distribution</li>
<li><code>"logistic</code>: logistic distribution as parameterized in <code><a href="/link/dlogis()?package=Surrogate&version=3.4.1" data-mini-rdoc="Surrogate::dlogis()">dlogis()</a></code></li>
<li><code>"t"</code>: student t distribution is parameterized in <code><a href="/link/dt()?package=Surrogate&version=3.4.1" data-mini-rdoc="Surrogate::dt()">dt()</a></code></li>
<li><code>"lognormal"</code>: lognormal distribution as parameterized in <code><a href="/link/dlnorm()?package=Surrogate&version=3.4.1" data-mini-rdoc="Surrogate::dlnorm()">dlnorm()</a></code></li>
<li><code>"gamma"</code>: gamma distribution as parameterized in <code><a href="/link/dgamma()?package=Surrogate&version=3.4.1" data-mini-rdoc="Surrogate::dgamma()">dgamma()</a></code></li>
<li><code>"weibull"</code>: weibull distribution as parameterized in <code><a href="/link/dweibull()?package=Surrogate&version=3.4.1" data-mini-rdoc="Surrogate::dweibull()">dweibull()</a></code></li>
</ul></dd>
<dt>fix.arg</dt>
<dd>An optional named list giving the values of fixed parameters
of the named distribution or a function of data computing (fixed) parameter
values and returning a named list. Parameters with fixed value are thus NOT
estimated by this maximum likelihood procedure.</dd></dl>

Arguments

Fit marginal distribution — marginal_distribution

<dl>

<dt>x</dt>
<dd>(numeric) data vector</dd>


<dt>distribution</dt>
<dd>Distributional family. One of the follwing:<ul>
<li><code>"normal"</code>: normal distribution</li>
<li><code>"logistic</code>: logistic distribution as parameterized in <code><a href='https://rdrr.io/r/stats/Logistic.html'>dlogis()</a></code></li>
<li><code>"t"</code>: student t distribution is parameterized in <code><a href='https://rdrr.io/r/stats/TDist.html'>dt()</a></code></li>
<li><code>"lognormal"</code>: lognormal distribution as parameterized in <code><a href='https://rdrr.io/r/stats/Lognormal.html'>dlnorm()</a></code></li>
<li><code>"gamma"</code>: gamma distribution as parameterized in <code><a href='https://rdrr.io/r/stats/GammaDist.html'>dgamma()</a></code></li>
<li><code>"weibull"</code>: weibull distribution as parameterized in <code><a href='https://rdrr.io/r/stats/Weibull.html'>dweibull()</a></code></li>
</ul></dd>


<dt>fix.arg</dt>
<dd>An optional named list giving the values of fixed parameters
of the named distribution or a function of data computing (fixed) parameter
values and returning a named list. Parameters with fixed value are thus NOT
estimated by this maximum likelihood procedure.</dd>

</dl>

Fit marginal distribution

marginal_distribution: Fit marginal distribution

Description

Usage

Value

Arguments