psychopdaROC Test Data - Basic Diagnostic Test — psychopdaROC

Basic diagnostic test dataset with 200 patients for ROC analysis. Contains binary disease status and a continuous biomarker with moderate discrimination (AUC ~0.75-0.80).

Usage

psychopdaROC_test

Format

A data frame with 200 rows and 5 variables:

patient_id: Character: Patient identifier (PT001-PT200)
disease_status: Factor: "Disease" or "Healthy" (30%/70% prevalence)
biomarker: Numeric: Continuous biomarker value (mean: 75 for diseased, 50 for healthy)
age: Numeric: Patient age in years (mean 60, SD 12)
sex: Factor: "Male" or "Female"

Source

Generated test data for ClinicoPath package

Details

Biomarker values follow normal distributions with clear separation between disease groups, suitable for demonstrating basic ROC curve analysis and optimal cutpoint determination using Youden index or other metrics.

Examples

data(psychopdaROC_test)
psychopdaROC(data = psychopdaROC_test, dependentVars = "biomarker",
             classVar = "disease_status", positiveClass = "Disease",
             refVar = "biomarker")
#> 
#>  ADVANCED ROC ANALYSIS
#> 
#> 
#> 
#> 
#>  Procedure Notes
#> 
#> 
#> 
#>  The ROC analysis has been completed using the following
#>  specifications:
#> 
#>   
#> 
#>  Measure Variable(s): biomarker
#> 
#>  Class Variable: disease_status
#> 
#>  Positive Class: Disease
#> 
#>   
#> 
#>  Method: maximize_metric
#> 
#>  All Observed Cutpoints: FALSE
#> 
#>  Metric: youden
#> 
#>  Direction (relative to cutpoint): >=
#> 
#>  Tie Breakers: mean
#> 
#>  Metric Tolerance: 1e-06
#> 
#>   
#> 
#>  <hr />
#> 
#>  <div style='padding: 10px; background-color: #f8f9fa; border: 1px
#>  solid #dee2e6; border-radius: 4px; margin-bottom: 15px;'>
#> 
#>  Analysis Status
#> 
#>  Seed: 123Positive Class: Disease (Prevalence: 35.5%)Analysis Mode:
#>  Basic
#> 
#>  ROC Analysis Summary                                                     
#>  ──────────────────────────────────────────────────────────────────────── 
#>    Variable     AUC          95% CI Lower    95% CI Upper    p-value      
#>  ──────────────────────────────────────────────────────────────────────── 
#>    biomarker    0.8998799       0.8585411       0.9412187    < .0000001   
#>  ──────────────────────────────────────────────────────────────────────── 
#>    Note. Reading of the test values: <b>HIGHER values were taken to
#>    indicate Disease</b> (Classification Direction = ">="). If that is
#>    the wrong way round for this marker, every sensitivity,
#>    specificity, cutpoint and AUC below is reversed — switch
#>    Classification Direction to "<=" and the AUC becomes 1 minus the
#>    value shown.
#>    Note. AUC 95% confidence intervals computed using the DeLong
#>    method.
#> 
#> 
#>  Clinical Interpretation                                                                                                                                                                                                             
#>  ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
#>    Test         Performance Level    Clinical Recommendation                                Detailed Interpretation                                                                                                                  
#>  ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
#>    biomarker    Good                 Suitable for clinical use with appropriate cutpoint    The test 'biomarker' has an AUC of 0.900 indicating good discriminatory ability. This test performs well for clinical decision making.   
#>  ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
#> 
#> 
#>  OPTIMAL CUTPOINTS AND PERFORMANCE
#> 
#>  no title                                                                                                          
#>  ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
#>    Cutpoint      Sensitivity    Specificity    PPV          NPV          Youden's J    AUC          Metric Score   
#>  ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
#>    54.2904443       94.36620       68.99225     62.61682     95.69892     0.6335845    0.8998799       0.6335845   
#>  ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
#> 
#> 
#>  Area Under the ROC Curve                                                 
#>  ──────────────────────────────────────────────────────────────────────── 
#>    Variable     AUC          95% CI Lower    95% CI Upper    p-value      
#>  ──────────────────────────────────────────────────────────────────────── 
#>    biomarker    0.8998799       0.8585411       0.9412187    < .0000001   
#>  ──────────────────────────────────────────────────────────────────────── 
#>    Note. Reading of the test values: <b>HIGHER values were taken to
#>    indicate Disease</b> (Classification Direction = ">="). If that is
#>    the wrong way round for this marker, every sensitivity,
#>    specificity, cutpoint and AUC below is reversed — switch
#>    Classification Direction to "<=" and the AUC becomes 1 minus the
#>    value shown.
#>    Note. AUC 95% confidence intervals computed using the DeLong
#>    method.
#>