ordinal_to_cutpoints

<code>ordinal_to_cutpoints()</code> converts the ordinal endpoints to the corresponding
cutpoints of the underlying latent continuous variable. Let
\(P(x \le k) = G(c_k)\) where \(G\) is the distribution function of the
latent variable. <code>ordinal_to_cutpoints()</code> converts \(x\) to \(c_k\) (or to
\(c_{k - 1}\)) if <code>strict = TRUE</code>.

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

ordinal_to_cutpoints function

<dl><dt>x</dt>
<dd>Integer vector with values in <code>1:(length(cutpoints) + 1)</code>.</dd>
<dt>cutpoints</dt>
<dd>The cutpoints on the latent scale corresponding to
\(\boldsymbol{c} = c(c_1, \cdots, c_{K - 1})\).</dd>
<dt>strict</dt>
<dd>(boolean) See function description.</dd></dl>

Arguments

Convert Ordinal Observations to Latent Cutpoints — ordinal_to_cutpoints

<dl>

<dt>x</dt>
<dd>Integer vector with values in <code>1:(length(cutpoints) + 1)</code>.</dd>


<dt>cutpoints</dt>
<dd>The cutpoints on the latent scale corresponding to
\(\boldsymbol{c} = c(c_1, \cdots, c_{K - 1})\).</dd>


<dt>strict</dt>
<dd>(boolean) See function description.</dd>

</dl>

Convert Ordinal Observations to Latent Cutpoints

ordinal_to_cutpoints: Convert Ordinal Observations to Latent Cutpoints

Description

Usage

Value

Arguments