Restrictions.BinBin

The function <code>Restrictions.BinBin</code> gives an overview of the restrictions in \(\bold{\pi}_{f}\)
under different assumptions regarding montonicity when both \(S\) and \(T\) are binary.

Causal-Inference framework

Counterfactuals

Single-trial setting

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

Restrictions.BinBin function

<dl> <dt>pi1_1_</dt>
<dd>A scalar that contains \(P(T=1,S=1|Z=0)\), i.e., the proability that \(S=T=1\) when under treatment \(Z=0\).</dd> <dt>pi1_0_</dt>
<dd>A scalar that contains \(P(T=1,S=0|Z=0)\).</dd> <dt>pi_1_1</dt>
<dd>A scalar that contains \(P(T=1,S=1|Z=1)\).</dd> <dt>pi_1_0</dt>
<dd>A scalar that contains \(P(T=1,S=0|Z=1)\).</dd> <dt>pi0_1_</dt>
<dd>A scalar that contains \(P(T=0,S=1|Z=0)\).</dd> <dt>pi_0_1</dt>
<dd>A scalar that contains \(P(T=0,S=1|Z=1)\).</dd> </dl>

Arguments

Wim Van der Elst, Ariel Alonso, &amp; Geert Molenberghs

Author

Examine restrictions in \(\bold{\pi}_{f}\) under different montonicity assumptions for binary \(S\) and \(T\) — Restrictions.BinBin

<dl>

 <dt>pi1_1_</dt>
<dd>A scalar that contains \(P(T=1,S=1|Z=0)\), i.e., the proability that \(S=T=1\) when under treatment \(Z=0\).</dd>

 <dt>pi1_0_</dt>
<dd>A scalar that contains \(P(T=1,S=0|Z=0)\).</dd>

 <dt>pi_1_1</dt>
<dd>A scalar that contains \(P(T=1,S=1|Z=1)\).</dd>

 <dt>pi_1_0</dt>
<dd>A scalar that contains \(P(T=1,S=0|Z=1)\).</dd>

 <dt>pi0_1_</dt>
<dd>A scalar that contains \(P(T=0,S=1|Z=0)\).</dd>

 <dt>pi_0_1</dt>
<dd>A scalar that contains \(P(T=0,S=1|Z=1)\).</dd>

 </dl>

Examine restrictions in \(\bold{\pi}_{f}\) under different montonicity assumptions for binary \(S\) and \(T\)

Restrictions.BinBin: Examine restrictions in \(\bold{\pi}_{f}\) under different montonicity assumptions for binary \(S\) and \(T\)

Description

Usage

Value

Arguments

Author

References

See Also

Examples