Diagnostic Test Accuracy Assessment

Computes a 2×2 confusion matrix and comprehensive diagnostic performance metrics for a binary classification test, with exact binomial confidence intervals.

Usage

diag_test(
  data,
  test,
  ref,
  positive = NULL,
  test_positive = NULL,
  conf.level = 0.95
)

Arguments

data: A data.frame containing test and ref variables.
test: Unquoted name of the diagnostic test variable (must be binary).
ref: Unquoted name of the reference standard variable (must be binary).
positive: Character or numeric. Level representing "Positive" in the reference variable. If NULL (default), auto-detected from common positive labels ("Yes", "1", "Positive", etc.) or the last level.
test_positive: Character or numeric. Level representing "Positive" in the test variable. If NULL (default), mirrors positive when the same label exists in the test variable, then falls back to auto-detection.
conf.level: Numeric. Confidence level for binomial CIs (0–1). Default: 0.95.

Value

An object of class diag_test — a named list with:

$table: 2×2 table object (Test × Ref).
$stats: data.frame with columns Metric, Estimate, LowerCI, UpperCI.
$labels: named list with ref_pos, ref_neg, test_pos, test_neg.
$sample_size: integer, total valid observations.
$conf.level: numeric, confidence level used.

Details

Confusion Matrix Layout

           | Ref +   | Ref -
-----------+---------+--------
Test +     |   TP    |   FP
Test -     |   FN    |   TN

Metrics Computed

Sensitivity (Recall) = TP / (TP + FN)
Specificity = TN / (TN + FP)
PPV (Precision) = TP / (TP + FP)
NPV = TN / (TN + FN)
Accuracy = (TP + TN) / Total
Prevalence = (TP + FN) / Total
Likelihood Ratio + = Sensitivity / (1 – Specificity)
Likelihood Ratio – = (1 – Sensitivity) / Specificity
Youden's Index = Sensitivity + Specificity – 1
F1 Score = 2 × (PPV × Sensitivity) / (PPV + Sensitivity)

Binomial CIs (exact Clopper–Pearson) are computed for the first six metrics. Likelihood Ratios, Youden's Index, and F1 Score do not have CIs.

Examples

set.seed(1)
n   <- 200
ref <- factor(sample(c("No", "Yes"), n, replace = TRUE, prob = c(.55, .45)))
tst <- ifelse(ref == "Yes",
              ifelse(runif(n) < .80, "Yes", "No"),
              ifelse(runif(n) < .85, "No",  "Yes"))
df  <- data.frame(rapid_test = factor(tst), lab = ref)

result <- diag_test(df, test = rapid_test, ref = lab,
                    positive = "Yes", test_positive = "Yes")
print(result)
#> 
#> ============================================================
#>   DIAGNOSTIC TEST EVALUATION
#> ============================================================
#> 
#>   Sample size      : 200
#>   Confidence level : 95%
#> 
#>   Reference standard (gold standard):
#>     Positive = 'Yes'   |   Negative = 'No'
#> 
#>   Diagnostic test:
#>     Positive = 'Yes'   |   Negative = 'No'
#> 
#> ------------------------------------------------------------
#>   Confusion Matrix
#> ------------------------------------------------------------
#>      Ref
#> Test  Yes No
#>   Yes  79 21
#>   No   14 86
#> 
#> ============================================================
#>   Performance Metrics  (95% CI)
#> ============================================================
#> Sensitivity           :  0.849  (0.760 – 0.915)
#> Specificity           :  0.804  (0.716 – 0.874)
#> Pos Pred Value (PPV)  :  0.790  (0.697 – 0.865)
#> Neg Pred Value (NPV)  :  0.860  (0.776 – 0.921)
#> Accuracy              :  0.825  (0.765 – 0.875)
#> Prevalence            :  0.465  (0.394 – 0.537)
#> ------------------------------------------------------------
#> Likelihood Ratio +    :  4.328
#> Likelihood Ratio -    :  0.187
#> Youden Index          :  0.653
#> F1 Score              :  0.819
#> 
as.data.frame(result)
#>                  Metric  Estimate   LowerCI   UpperCI
#> 1           Sensitivity 0.8494624 0.7603395 0.9151838
#> 2           Specificity 0.8037383 0.7157763 0.8742127
#> 3  Pos Pred Value (PPV) 0.7900000 0.6970846 0.8650563
#> 4  Neg Pred Value (NPV) 0.8600000 0.7762720 0.9212946
#> 5              Accuracy 0.8250000 0.7651160 0.8749756
#> 6            Prevalence 0.4650000 0.3943527 0.5367001
#> 7    Likelihood Ratio + 4.3282130        NA        NA
#> 8    Likelihood Ratio - 0.1872968        NA        NA
#> 9          Youden Index 0.6532007        NA        NA
#> 10             F1 Score 0.8186528        NA        NA