Find optimal symptom combinations for diagnosis (hierarchical/cluster-based) — optimize_combinations

Identifies the best symptom combinations for PTSD diagnosis where a specified number of symptoms must be present and must include at least one symptom from each defined cluster. This maintains the hierarchical structure of the diagnostic criteria while allowing configurable parameters.

Usage

optimize_combinations_clusters(
  data,
  n_symptoms = 6,
  n_required = 4,
  n_top = 3,
  score_by = "false_cases",
  clusters,
  DT = FALSE
)

Arguments

data

A dataframe containing exactly 20 columns with PCL-5 item scores (output of rename_ptsd_columns). Each symptom should be scored on a 0-4 scale where:

0 = Not at all
1 = A little bit
2 = Moderately
3 = Quite a bit
4 = Extremely

n_symptoms

Integer specifying how many symptoms per combination (default: 6). Must be at least as large as the number of clusters.

n_required

Integer specifying how many symptoms must be present for diagnosis (default: 4). Must be between 1 and n_symptoms.

n_top

Integer specifying how many top combinations to return (default: 3). Must be a positive integer.

score_by

Character string specifying optimization criterion:

"false_cases": Minimize total misclassifications
"newly_nondiagnosed": Minimize false negatives only

clusters

A named list of integer vectors defining the cluster structure. Each list element represents one cluster, with the integer vector specifying which symptom indices belong to that cluster. Cluster elements must not overlap. This parameter is required (no default).

For PCL-5: list(B = 1:5, C = 6:7, D = 8:14, E = 15:20)

DT

Logical. If TRUE, return the summary as an interactive datatable widget. If FALSE (default), return a plain data.frame. The DT package must be installed when DT = TRUE.

Value

A list containing:

best_symptoms: List of n_top vectors, each containing n_symptoms symptom numbers representing the best combinations found
diagnosis_comparison: Dataframe comparing original DSM-5 diagnosis with diagnoses based on the best combinations
summary: Diagnostic accuracy metrics for each combination. A data.frame by default, or an interactive datatable if DT = TRUE.

Details

The function:

Generates valid combinations ensuring representation from all clusters
Requires n_required symptoms to be present (>=2 on original 0-4 scale) for diagnosis
Validates that present symptoms include at least one from each cluster
Identifies the n_top combinations that best match the original DSM-5 diagnosis

The clusters parameter must be a named list specifying the cluster structure. For PCL-5, the standard clusters are:

Cluster B (Intrusion): Items 1-5
Cluster C (Avoidance): Items 6-7
Cluster D (Negative alterations in cognitions and mood): Items 8-14
Cluster E (Alterations in arousal and reactivity): Items 15-20

Optimization can be based on either:

Minimizing false cases (both false positives and false negatives)
Minimizing only false negatives (newly non-diagnosed cases)

Examples

# Create example data
ptsd_data <- data.frame(matrix(sample(0:4, 200, replace=TRUE), ncol=20))
names(ptsd_data) <- paste0("symptom_", 1:20)

# \donttest{
# Find best hierarchical combinations with PCL-5 clusters
pcl5_clusters <- list(B = 1:5, C = 6:7, D = 8:14, E = 15:20)
results <- optimize_combinations_clusters(ptsd_data, n_symptoms = 6,
             n_required = 4, score_by = "false_cases", clusters = pcl5_clusters)
#> ℹ Generated 13685 valid cluster-constrained combinations
#> ℹ Evaluated 13685 combinations. Best: 1, 4, 6, 9, 15, 20

# Get symptom numbers
results$best_symptoms
#> [[1]]
#> [1]  1  4  6  9 15 20
#> 
#> [[2]]
#> [1]  2  4  6  9 15 20
#> 
#> [[3]]
#> [1]  1  4  6  9 17 18
#> 

# View summary statistics
results$summary
#>                Scenario combination_id rank Total Diagnosed Total Non-Diagnosed
#> 1             PTSD_orig           <NA>   NA       10 (100%)              0 (0%)
#> 2 symptom_1_4_6_9_15_20  1_4_6_9_15_20    1         7 (70%)             3 (30%)
#> 3 symptom_2_4_6_9_15_20  2_4_6_9_15_20    2         7 (70%)             3 (30%)
#> 4 symptom_1_4_6_9_17_18  1_4_6_9_17_18    3         7 (70%)             3 (30%)
#>   True Positive True Negative Newly Diagnosed Newly Non-Diagnosed True Cases
#> 1            10             0               0                   0         10
#> 2             7             0               0                   3          7
#> 3             7             0               0                   3          7
#> 4             7             0               0                   3          7
#>   False Cases Sensitivity Specificity PPV NPV
#> 1           0         1.0          NA   1  NA
#> 2           3         0.7          NA   1   0
#> 3           3         0.7          NA   1   0
#> 4           3         0.7          NA   1   0
# }