Find optimal non-hierarchical six-symptom combinations for PTSD diagnosis

`r lifecycle::badge("deprecated")`

Convenience wrapper around optimize_combinations with the original PCL-5 defaults: 6 symptoms, 4 required, top 3 returned.

Identifies the three best six-symptom combinations for PTSD diagnosis where any four symptoms must be present, regardless of their cluster membership.

Usage

analyze_best_six_symptoms_four_required(
  data,
  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

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.

Value

A list containing:

• best_symptoms: List of three vectors, each containing six symptom numbers representing the best combinations found

• diagnosis_comparison: Dataframe comparing original DSM-5 diagnosis with diagnoses based on the three 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 6 symptoms from the 20 PCL-5 items

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

3. Identifies the three 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)

See also

optimize_combinations for the generalized version with configurable parameters.

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 combinations minimizing false cases
results <- analyze_best_six_symptoms_four_required(ptsd_data, score_by = "false_cases")
#> Warning: `analyze_best_six_symptoms_four_required()` was deprecated in PTSDdiag 0.2.1.
#> ℹ Please use `optimize_combinations()` instead.
#> ℹ Evaluated 38760 combinations. Best: 1, 2, 3, 6, 11, 16

# Get symptom numbers
results$best_symptoms
#> [[1]]
#> [1]  1  2  3  6 11 16
#> 
#> [[2]]
#> [1]  1  2  3  8 11 13
#> 
#> [[3]]
#> [1]  1  2  3  8 11 16
#> 

# View raw comparison data
results$diagnosis_comparison
#>    PTSD_orig symptom_1_2_3_6_11_16 symptom_1_2_3_8_11_13 symptom_1_2_3_8_11_16
#> 1      FALSE                 FALSE                 FALSE                 FALSE
#> 2       TRUE                  TRUE                  TRUE                  TRUE
#> 3       TRUE                  TRUE                  TRUE                  TRUE
#> 4       TRUE                  TRUE                  TRUE                  TRUE
#> 5       TRUE                  TRUE                  TRUE                  TRUE
#> 6      FALSE                 FALSE                 FALSE                 FALSE
#> 7       TRUE                  TRUE                  TRUE                  TRUE
#> 8      FALSE                 FALSE                 FALSE                 FALSE
#> 9      FALSE                 FALSE                 FALSE                 FALSE
#> 10      TRUE                  TRUE                  TRUE                  TRUE

# View summary statistics
results$summary
#>                Scenario combination_id rank Total Diagnosed Total Non-Diagnosed
#> 1             PTSD_orig           <NA>   NA         6 (60%)             4 (40%)
#> 2 symptom_1_2_3_6_11_16  1_2_3_6_11_16    1         6 (60%)             4 (40%)
#> 3 symptom_1_2_3_8_11_13  1_2_3_8_11_13    2         6 (60%)             4 (40%)
#> 4 symptom_1_2_3_8_11_16  1_2_3_8_11_16    3         6 (60%)             4 (40%)
#>   True Positive True Negative Newly Diagnosed Newly Non-Diagnosed True Cases
#> 1             6             4               0                   0         10
#> 2             6             4               0                   0         10
#> 3             6             4               0                   0         10
#> 4             6             4               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
# }