Build a tidy comparison table of top combinations across scenarios

Produces a manuscript-ready table summarising the diagnostic performance of each top combination (or fixed criterion) in a compare_optimizations result. The output matches the layout of the PTSDdiag preprint's Table 2: one row per combination, with Approach / Rank / Combination / TP / FN / FP / TN / Sensitivity / Specificity / PPV / NPV / Accuracy / Balanced Accuracy.

Usage

summarize_top_combinations(comparison, top_n = NULL, as_percent = FALSE)

Arguments

comparison: A ptsdiag_comparison object.
top_n: Optional integer. Per-scenario limit on combinations to include. Fixed scenarios always contribute exactly one row. Default NULL returns all stored combinations.
as_percent: Logical. If TRUE, Sensitivity/Specificity/PPV/NPV/Accuracy/Balanced Accuracy are returned as percentages (0-100); otherwise as fractions (0-1). Default FALSE.

Value

A data.frame with columns: Approach, Rank, Combination, TP, FN, FP, TN, Sensitivity, Specificity, PPV, NPV, Accuracy, Balanced Accuracy.

Details

For each scenario, the per-row diagnosis_comparison dataframe is summarised via summarize_ptsd_changes. The self-comparison PTSD_orig row is dropped, the remaining rows are renamed, and the scenario label is prepended.

Sensitivity, specificity, PPV, NPV, accuracy and balanced accuracy are returned on the 0-1 fraction scale by default (matching compare_diagnostic_systems); set as_percent = TRUE to convert to 0-100 for manuscript display. Accuracy is (TP + TN) / N, the quantity maximised by score_by = "accuracy"; balanced accuracy is (sensitivity + specificity) / 2, the quantity maximised by the default score_by = "balanced_accuracy".

Examples

# \donttest{
# Use a 250-row subset and a small 4-symptom search to keep the example
# fast; omit `scenarios` to run the three default rules
ptsd_data <- rename_ptsd_columns(simulated_ptsd[1:250, ],
                                 id_col = c("patient_id", "age", "sex"))
comp <- compare_optimizations(
  ptsd_data,
  scenarios = list(
    "3/4 Non-hierarchical" = list(n_symptoms = 4, n_required = 3,
                                  hierarchical = FALSE)
  ),
  include_icd11 = TRUE, n_top = 5, show_progress = FALSE
)
#> ℹ Evaluated 4845 combinations. Best: 6, 7, 12, 17
summarize_top_combinations(comp, as_percent = TRUE)
#>               Approach Rank       Combination  TP FN FP TN Sensitivity
#> 1 3/4 Non-hierarchical    1 symptom_6_7_12_17 227  5  0 18    97.84483
#> 2 3/4 Non-hierarchical    2  symptom_4_6_7_12 226  6  0 18    97.41379
#> 3 3/4 Non-hierarchical    3  symptom_4_6_7_19 225  7  0 18    96.98276
#> 4 3/4 Non-hierarchical    4 symptom_6_7_12_13 225  7  0 18    96.98276
#> 5 3/4 Non-hierarchical    5 symptom_6_7_12_15 225  7  0 18    96.98276
#> 6               ICD-11    1        PTSD_icd11 220 12  2 16    94.82759
#>   Specificity      PPV      NPV Accuracy Balanced Accuracy
#> 1   100.00000 100.0000 78.26087     98.0          98.92241
#> 2   100.00000 100.0000 75.00000     97.6          98.70690
#> 3   100.00000 100.0000 72.00000     97.2          98.49138
#> 4   100.00000 100.0000 72.00000     97.2          98.49138
#> 5   100.00000 100.0000 72.00000     97.2          98.49138
#> 6    88.88889  99.0991 57.14286     94.4          91.85824
# }