compute_indicators

This function computes sensitivity, specificity, predictive values,
likelihood ratios, accuracy, and Youden index with confidence intervals
based on a 2x2 table of diagnostic test results.

Evaluate diagnostic test performance using data from laboratory or diagnostic research. It supports both binary and continuous test variables. It allows users to compute key performance indicators and visualize Receiver Operating Characteristic (ROC) curves, determine optimal cut-off thresholds, display confusion matrix, and export publication-ready plot. It aims to facilitate the application of statistical methods in diagnostic test evaluation by healthcare professionals. The methodology used to compute the performance indicators follows the overview described by Habibzadeh (2025) <doi:10.11613/BM.2025.010101>. Thanks to 'shiny' package.

Nassim AYAD

EvalTest

A 'shiny' App to Evaluate Diagnostic Tests Performance

compute_indicators function

<dl><dt>tp</dt>
<dd>True positives</dd>
<dt>fp</dt>
<dd>False positives</dd>
<dt>fn</dt>
<dd>False negatives</dd>
<dt>tn</dt>
<dd>True negatives</dd>
<dt>prev</dt>
<dd>Prevalence (numeric between 0 and 1)</dd>
<dt>conf</dt>
<dd>Confidence level (default 0.95)</dd></dl>

Arguments

Compute diagnostic test indicators — compute_indicators

<dl>

<dt>tp</dt>
<dd>True positives</dd>


<dt>fp</dt>
<dd>False positives</dd>


<dt>fn</dt>
<dd>False negatives</dd>


<dt>tn</dt>
<dd>True negatives</dd>


<dt>prev</dt>
<dd>Prevalence (numeric between 0 and 1)</dd>


<dt>conf</dt>
<dd>Confidence level (default 0.95)</dd>

</dl>

compute_indicators: Compute diagnostic test indicators

Description

Usage

Value

Arguments

Examples