diag_test() & roc(): Diagnostic Accuracy and Biomarker Discrimination

Diagnostic evaluations in clinical epidemiology assess how accurately an index test identifies the presence or absence of a target condition compared against a gold-standard reference.

In this guide, we follow the acute diagnostic phase of the ESTROBE-ACS study. Across six emergency departments, patients presenting with suspected acute coronary syndrome (ACS) were enrolled in a point-of-care (POC) biomarker substudy. We evaluate whether qualitative bedside point-of-care high-sensitivity troponin (poc_hstn_positive) or quantitative troponin concentrations (poc_hstn_value) can accurately rule out or identify adjudicated ACS (adjudicated_acs), adhering to the STARD 2015 (Standards for Reporting Diagnostic Accuracy Studies) guidelines.

1. Defining the Diagnostic Cohort

Clinical Context & Research Question: Within the ESTROBE-ACS emergency cohort, how was the point-of-care biomarker substudy sampled, and what are the observed test completion counts?

Before evaluating test performance, clinical investigators must document participant accounting:

# Subset participants enrolled in the bedside biomarker substudy
diagnostic <- subset(epitabl, diagnostic_substudy == "Yes")

c(
  total_enrolled = nrow(epitabl),
  substudy_enrolled = nrow(diagnostic),
  observed_index_tests = sum(!is.na(diagnostic$poc_hstn_positive)),
  observed_reference_standards = sum(!is.na(diagnostic$adjudicated_acs))
)
#>               total_enrolled            substudy_enrolled 
#>                         1500                          920 
#>         observed_index_tests observed_reference_standards 
#>                          899                          920

2. Binary Diagnostic Test Accuracy with diag_test()

What is the clinical accuracy (Sensitivity, Specificity, PPV, NPV, and Likelihood Ratios) of a qualitative bedside troponin test for detecting adjudicated ACS?

diag_test() evaluates binary index tests against a reference standard. To ensure clinical transparency, you must explicitly declare which factor levels represent positive status: - positive: The target condition level in the reference standard ("Yes"). - test_positive: The positive result level in the index test ("Positive").

accuracy <- diag_test(
  diagnostic,
  test = poc_hstn_positive,
  ref = adjudicated_acs,
  positive = "Yes",
  test_positive = "Positive",
  ci = "exact"
)
#> Removed 21 observation(s) with missing values (2.3%).
accuracy
#> 
#> ============================================================
#>   DIAGNOSTIC TEST EVALUATION
#> ============================================================
#> 
#>   Sample size      : 899
#>   Confidence level : 95%
#>   CI method        : exact
#> 
#>   Reference standard (gold standard):
#>     Positive = 'Yes'   |   Negative = 'No'
#> 
#>   Diagnostic test:
#>     Positive = 'Positive'   |   Negative = 'Negative'
#> 
#> ------------------------------------------------------------
#>   Confusion Matrix
#> ------------------------------------------------------------
#>           Ref
#> Test       Yes  No
#>   Positive 249  56
#>   Negative  95 499
#> 
#> ============================================================
#>   Performance Metrics  (95% CI)
#> ============================================================
#> Sensitivity           :    72.38%  (67.33% - 77.04%)
#> Specificity           :    89.91%  (87.10% - 92.29%)
#> Pos Pred Value (PPV)  :    81.64%  (76.83% - 85.82%)
#> Neg Pred Value (NPV)  :    84.01%  (80.81% - 86.86%)
#> Accuracy              :    83.20%  (80.60% - 85.59%)
#> Prevalence            :    38.26%  (35.07% - 41.53%)
#> ------------------------------------------------------------
#> Likelihood Ratio +    :      7.17  (5.55 - 9.27)
#> Likelihood Ratio -    :      0.31  (0.26 - 0.37)
#> Youden Index          :      0.62
#> F1 Score              :      0.77
#> Diagnostic Odds Ratio :     23.36  (16.24 - 33.59)
#> Cohen's Kappa         :      0.64  (0.58 - 0.69)

Key Metrics Computed:

2.1 Confusion Matrix Visualization

SimtablR provides a fourfold display of the confusion matrix via plot():

plot(accuracy, main = "Point-of-Care Troponin Confusion Matrix")

Fourfold plot showing true positives, false positives, false negatives, and true negatives

The four quadrants represent True Positives, False Positives, False Negatives, and True Negatives, visually scaling cell counts and odds ratios.

2.2 Tidy Metric Extraction

All underlying metrics, standard errors, and confidence intervals are stored without destructive rounding in $data and can be extracted as a tidy data frame:

# Extract structured evidence table
head(as.data.frame(accuracy, tidy = TRUE), 8)
#>                           metric  estimate  conf.low conf.high
#> sensitivity          Sensitivity 0.7238372 0.6733423 0.7704311
#> specificity          Specificity 0.8990991 0.8709753 0.9228776
#> ppv         Pos Pred Value (PPV) 0.8163934 0.7682959 0.8582225
#> npv         Neg Pred Value (NPV) 0.8400673 0.8080707 0.8686349
#> accuracy                Accuracy 0.8320356 0.8059641 0.8559089
#> prevalence            Prevalence 0.3826474 0.3507490 0.4153175
#> lr_pos        Likelihood Ratio + 7.1737438 5.5491609 9.2739425
#> lr_neg        Likelihood Ratio - 0.3071550 0.2582723 0.3652897

3. Continuous Biomarker Discrimination with roc()

How well does quantitative bedside troponin concentration discriminate adjudicated ACS across the entire spectrum of decision cutoffs, and what is the optimal threshold by Youden’s J index?

When an index marker is measured on a continuous numerical scale (such as poc_hstn_value in ng/L), roc() computes the Receiver Operating Characteristic curve.

3.1 Empirical AUC and DeLong Confidence Intervals

Evaluating the marker without cutpoint hunting provides an unbiased assessment of overall discriminatory capacity:

troponin_roc <- roc(
  diagnostic,
  marker = poc_hstn_value,
  outcome = adjudicated_acs,
  positive = "Yes",
  cutpoint = "none"
)
#> Removed 21 observation(s) with missing values (2.3%).
troponin_roc
#> 
#> ROC Curve Analysis
#> ==================
#> Outcome: adjudicated_acs (positive = 'Yes')
#> CI method: delong | Direction: auto
#> 
#>          Marker       AUC (95% CI) Direction Cutpoint Sensitivity Specificity
#>  poc_hstn_value 0.90 (0.89 - 0.92)         <                                 
#>  PPV NPV
#> 
#> ℹ Methodological guidance
#>   2 additional methodological notes hidden. Run simtablr_guidance("teaching")
#>   separately before printing to show all advice.

The estimated Area Under the Curve (AUC) is reported alongside asymptotic DeLong 95% confidence intervals.

3.2 Threshold Optimization with Youden’s J

In clinical practice, emergency physicians often require a single decision cutoff to trigger admission or discharge. Passing cutpoint = "youden" optimizes the trade-off between Sensitivity and Specificity by maximizing Youden’s \(J = \text{Sensitivity} + \text{Specificity} - 1\):

troponin_youden <- roc(
  diagnostic,
  marker = poc_hstn_value,
  outcome = adjudicated_acs,
  positive = "Yes",
  cutpoint = "youden"
)
#> Removed 21 observation(s) with missing values (2.3%).
troponin_youden
#> 
#> ROC Curve Analysis
#> ==================
#> Outcome: adjudicated_acs (positive = 'Yes')
#> CI method: delong | Direction: auto
#> 
#>          Marker       AUC (95% CI) Direction Cutpoint Sensitivity Specificity
#>  poc_hstn_value 0.90 (0.89 - 0.92)         <    13.65      84.30%      81.08%
#>     PPV    NPV
#>  73.42% 89.29%
#> ! Methodological warning
#>   A data-driven ROC cutpoint is reported from the same data used to estimate
#>   performance; its sensitivity and specificity are optimistic. Validate the
#>   threshold in external data or with resampling before treating it as a
#>   decision rule.
#>   2 additional methodological notes hidden. Run simtablr_guidance("teaching")
#>   separately before printing to show all advice.

SimtablR automatically displays educational advice reminding analysts that data-derived thresholds exhibit optimistic bias and require external cohort validation before clinical deployment.

3.3 Publication ROC Plot

SimtablR includes an autoplot() method that produces a publication-ready ROC curve:

ggplot2::autoplot(troponin_roc) +
  ggplot2::labs(
    title = "ROC Discrimination: Point-of-Care Troponin",
    subtitle = "ESTROBE-ACS Diagnostic Substudy"
  )

ROC curve displaying sensitivity versus 1 - specificity

4. Reporting Guidelines: STARD Checklist & Automated Methods

How do we verify methodological compliance against the STARD 2015 diagnostic checklist and generate publication-ready methods text?

SimtablR facilitates guideline adherence through the stard() reporting verb:

stard_report <- stard(accuracy)
head(as.data.frame(stard_report), 8)
#>   item                                 requirement       status pointer
#> 1    1 Identification as diagnostic accuracy study not-assessed        
#> 2    2                          Structured summary not-assessed        
#> 3    3          Scientific and clinical background not-assessed        
#> 4    4                            Study objectives not-assessed        
#> 5    5                                Study design not-assessed        
#> 6    6                        Eligibility criteria not-assessed        
#> 7    7                       Participant selection not-assessed        
#> 8    8                     Participant recruitment not-assessed

Furthermore, as_methods() translates the exact analytical choices, confidence interval formulas, and software implementations into concise prose ready for manuscript submission:

cat(as_methods(accuracy))
#> Diagnostic accuracy was evaluated against adjudicated_acs as the reference standard; sensitivity, specificity, predictive values, accuracy, and prevalence were reported with exact binomial 95% CI, likelihood ratios with asymptotic (log-method) 95% confidence intervals, the diagnostic odds ratio with a log 95% CI, and Cohen's kappa was reported for index-test / reference-standard agreement with a large-sample 95% CI.