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Find optimal symptom combinations for diagnosis (non-hierarchical)

Source: R/analysis.R
optimize_combinations.Rd

Identifies the best symptom combinations for PTSD diagnosis where a specified number of symptoms must be present, regardless of their cluster membership. This is a generalized version that allows configuring the number of symptoms per combination, the required threshold, and how many top results to return.

Usage

optimize_combinations(
  data,
  n_symptoms = 6,
  n_required = 4,
  n_top = 3,
  score_by = "false_cases",
  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 between 1 and 20.

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

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:

  1. Tests all possible combinations of n_symptoms symptoms from the 20 PCL-5 items

  2. Requires n_required symptoms to be present (>=2 on original 0-4 scale) for diagnosis

  3. Identifies the n_top combinations that best match the original DSM-5 diagnosis

Optimization can be based on either:

  • Minimizing false cases (both false positives and false negatives)

  • Minimizing only false negatives (newly non-diagnosed cases)

The symptom clusters in PCL-5 are:

  • Items 1-5: Intrusion symptoms (Criterion B)

  • Items 6-7: Avoidance symptoms (Criterion C)

  • Items 8-14: Negative alterations in cognitions and mood (Criterion D)

  • Items 15-20: Alterations in arousal and reactivity (Criterion E)

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 6-symptom combinations requiring 4 present (classic defaults)
results <- optimize_combinations(ptsd_data, n_symptoms = 6, n_required = 4,
             score_by = "false_cases")
#> ℹ Evaluated 38760 combinations. Best: 1, 2, 4, 7, 15, 16

# Find best 5-symptom combinations requiring 3 present, return top 5
results2 <- optimize_combinations(ptsd_data, n_symptoms = 5, n_required = 3,
              n_top = 5, score_by = "false_cases")
#> ℹ Evaluated 15504 combinations. Best: 1, 4, 7, 10, 15

# Get symptom numbers
results$best_symptoms
#> [[1]]
#> [1]  1  2  4  7 15 16
#> 
#> [[2]]
#> [1]  1  2  6  7  8 15
#> 
#> [[3]]
#> [1]  1  2  6  7 14 15
#> 

# View summary statistics
results$summary
#>                Scenario combination_id rank Total Diagnosed Total Non-Diagnosed
#> 1             PTSD_orig           <NA>   NA         5 (50%)             5 (50%)
#> 2 symptom_1_2_4_7_15_16  1_2_4_7_15_16    1         5 (50%)             5 (50%)
#> 3  symptom_1_2_6_7_8_15   1_2_6_7_8_15    2         5 (50%)             5 (50%)
#> 4 symptom_1_2_6_7_14_15  1_2_6_7_14_15    3         5 (50%)             5 (50%)
#>   True Positive True Negative Newly Diagnosed Newly Non-Diagnosed True Cases
#> 1             5             5               0                   0         10
#> 2             5             5               0                   0         10
#> 3             5             5               0                   0         10
#> 4             5             5               0                   0         10
#>   False Cases Sensitivity Specificity PPV NPV
#> 1           0           1           1   1   1
#> 2           0           1           1   1   1
#> 3           0           1           1   1   1
#> 4           0           1           1   1   1
# }