targetDel

Find the difference between the average subpopulation treatment effect and the desired one

Enrichment strategies play a critical role in modern clinical trial design, especially as precision medicine advances the focus on patient-specific efficacy. Recent developments in enrichment design have introduced biomarker randomness and accounted for the correlation structure between treatment effect and biomarker, resulting in a two-stage threshold enrichment design. We propose novel two-stage enrichment designs capable of handling two or more continuous biomarkers.
See Zhang, F. and Gou, J. (2025). Using multiple biomarkers for patient enrichment in two-stage clinical designs. Technical Report.

Jiangtao Gou

cobenrich

Using Multiple Continuous Biomarkers for Patient Enrichment in
Two-Stage Clinical Designs

Fengqing (Zoe) Zhang

targetDel function

<dl><dt>z2</dt>
<dd>the standardized biomarker value of the second biomarker</dd>
<dt>kkk</dt>
<dd>the researchers' weighting preference between the two biomarkers</dd>
<dt>muminusmu0</dt>
<dd>a number of the difference between the mean of outcome and the minimal clinically important treatment effect</dd>
<dt>kappa</dt>
<dd>a number of the correlation coefficient between two biomarkers</dd>
<dt>rhovec</dt>
<dd>a numeric vector of two correlation coefficients between the output and two biomarkers</dd>
<dt>sigma</dt>
<dd>a number of the standard deviation of outcome</dd>
<dt>cDel</dt>
<dd>the desired average subpopulation treatment effect</dd></dl>

Arguments

Find the difference between the average subpopulation treatment effect and the desired one — targetDel

<dl>

<dt>z2</dt>
<dd>the standardized biomarker value of the second biomarker</dd>


<dt>kkk</dt>
<dd>the researchers' weighting preference between the two biomarkers</dd>


<dt>muminusmu0</dt>
<dd>a number of the difference between the mean of outcome and the minimal clinically important treatment effect</dd>


<dt>kappa</dt>
<dd>a number of the correlation coefficient between two biomarkers</dd>


<dt>rhovec</dt>
<dd>a numeric vector of two correlation coefficients between the output and two biomarkers</dd>


<dt>sigma</dt>
<dd>a number of the standard deviation of outcome</dd>


<dt>cDel</dt>
<dd>the desired average subpopulation treatment effect</dd>

</dl>

targetDel: Find the difference between the average subpopulation treatment effect and the desired one

Description

Usage

Value

Arguments