getOC

Given a set of data including survival time ,censor status and Biomarker values,
return the Optimal cutoff for the biomarker.

The Biomarker Optimal Segmentation System R package, 'bossR', is designed for precision medicine, helping to identify individual traits using biomarkers. It focuses on determining the most effective cutoff value for a continuous biomarker, which is crucial for categorizing patients into two groups with distinctly different clinical outcomes. The package simultaneously finds the optimal cutoff from given candidate values and tests its significance. Simulation studies demonstrate that 'bossR' offers statistical power and false positive control non-inferior to the permutation approach (considered the gold standard in this field), while being hundreds of times faster.

Xuekui Zhang

bossR

Biomarker Optimal Segmentation System

Liuyi Lan

Xing Li

Xuanjin Cheng

getOC function

<dl><dt>data</dt>
<dd>A data frame which contains 3 columns for cox regression : survival time, censor status, Biomarker values.
2 columns for linear regression : Y, X.</dd>
<dt>cutoff</dt>
<dd>Numeric vector of candidate cutoffs.</dd>
<dt>type</dt>
<dd>A number; if =1, will perform linear regression;if =2(default) will perform cox regerssion.</dd></dl>

Arguments

Select Optimal cutoff for a biomarker — getOC

<dl>

<dt>data</dt>
<dd>A data frame which contains 3 columns for cox regression : survival time, censor status, Biomarker values.
2 columns for linear regression : Y, X.</dd>


<dt>cutoff</dt>
<dd>Numeric vector of candidate cutoffs.</dd>


<dt>type</dt>
<dd>A number; if =1, will perform linear regression;if =2(default) will perform cox regerssion.</dd>

</dl>

getOC: Select Optimal cutoff for a biomarker

Description

Usage

Value

Arguments

References

Examples