estimate_ICA_BinCont

<code>estimate_ICA_BinCont()</code> estimates the individual causal association (ICA)
for a sample of individual causal treatment effects with a continuous
surrogate and a binary true endpoint. The ICA in this setting is defined as
follows, $$R^2_H = \frac{I(\Delta S; \Delta T)}{H(\Delta T)}$$ where
$I(\Delta S; \Delta T)$ is the mutual information and $H(\Delta T)$
the entropy.

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_ICA_BinCont function

<dl><dt>delta_S</dt>
<dd>(numeric) Vector of individual causal treatment effects on the
surrogate.</dd>
<dt>delta_T</dt>
<dd>(integer) Vector of individual causal treatment effects on the true
endpoint. Should take on one of the following values: <code>-1L</code>, <code>0L</code>, or <code>1L</code>.</dd></dl>

Arguments

Estimate ICA in Binary-Continuous Setting — estimate_ICA_BinCont

<dl>

<dt>delta_S</dt>
<dd>(numeric) Vector of individual causal treatment effects on the
surrogate.</dd>


<dt>delta_T</dt>
<dd>(integer) Vector of individual causal treatment effects on the true
endpoint. Should take on one of the following values: <code>-1L</code>, <code>0L</code>, or <code>1L</code>.</dd>

</dl>

Estimate ICA in Binary-Continuous Setting

estimate_ICA_BinCont: Estimate ICA in Binary-Continuous Setting

Description

Usage

Value

Arguments