Find optimal hierarchical six-symptom combinations for PTSD diagnosis
Source:R/analysis.R
analyze_best_six_symptoms_four_required_clusters.Rd`r lifecycle::badge("deprecated")`
Convenience wrapper around optimize_combinations_clusters with
the original PCL-5 defaults: 6 symptoms, 4 required, top 3 returned, and
standard DSM-5 cluster structure.
Identifies the three best six-symptom combinations for PTSD diagnosis where four symptoms must be present and must include at least one symptom from each DSM-5 criterion cluster.
Usage
analyze_best_six_symptoms_four_required_clusters(
data,
score_by = "balanced_accuracy",
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:
"balanced_accuracy": Maximise balanced accuracy, the mean of sensitivity and specificity. Robust when one diagnostic class is much more common than the other. Default.
"accuracy": Minimize total misclassifications (FP + FN, i.e. maximise overall accuracy).
"sensitivity": Minimize false negatives only (i.e. maximise sensitivity relative to the full DSM-5-TR diagnosis).
- DT
Logical. If
TRUE, return the summary as an interactivedatatablewidget. IfFALSE(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
datatableifDT = TRUE.
Details
The function:
Generates valid combinations ensuring representation from all clusters
Requires 4 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 three combinations that best match the original DSM-5 diagnosis
DSM-5 PTSD symptom clusters:
Cluster 1 (B) - Intrusion: Items 1-5
Cluster 2 (C) - Avoidance: Items 6-7
Cluster 3 (D) - Negative alterations in cognitions and mood: Items 8-14
Cluster 4 (E) - Alterations in arousal and reactivity: Items 15-20
Optimization can be based on:
Maximizing balanced accuracy, the mean of sensitivity and specificity (the default)
Minimizing false cases (both false positives and false negatives)
Minimizing only false negatives (newly non-diagnosed cases)
See also
optimize_combinations_clusters for the generalized
version with configurable parameters and custom cluster definitions.
Examples
# This deprecated wrapper always runs the full 6-symptom hierarchical
# search, so its example uses a 50-row subset to stay fast
ptsd_data <- rename_ptsd_columns(simulated_ptsd[1:50, ],
id_col = c("patient_id", "age", "sex"))
# \donttest{
# Find best hierarchical combinations with the default balanced-accuracy
# criterion
results <- analyze_best_six_symptoms_four_required_clusters(ptsd_data)
#> Warning: `analyze_best_six_symptoms_four_required_clusters()` was deprecated in PTSDdiag
#> 0.2.1.
#> ℹ Please use `optimize_combinations_clusters()` instead.
#> ℹ Generated 13685 valid cluster-constrained combinations
#> Evaluating combinations ■■■■■■■■ 24% | ETA: 3s
#> Evaluating combinations ■■■■■■■■■■■ 32% | ETA: 3s
#> Evaluating combinations ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 100% | ETA: 0s
#> ℹ Evaluated 13685 combinations. Best: 5, 6, 7, 11, 15, 16 (31 additional tied)
# Get symptom numbers
results$best_symptoms
#> [[1]]
#> [1] 5 6 7 11 15 16
#>
#> [[2]]
#> [1] 5 6 7 11 15 20
#>
#> [[3]]
#> [1] 5 6 7 13 15 16
#>
# View raw comparison data
results$diagnosis_comparison
#> patient_id age sex PTSD_orig symptom_5_6_7_11_15_16
#> 1 P0001 48 male TRUE TRUE
#> 2 P0002 29 male TRUE TRUE
#> 3 P0003 44 male TRUE TRUE
#> 4 P0004 41 female TRUE TRUE
#> 5 P0005 34 male TRUE TRUE
#> 6 P0006 18 male TRUE TRUE
#> 7 P0007 33 male TRUE TRUE
#> 8 P0008 30 female TRUE TRUE
#> 9 P0009 43 female TRUE TRUE
#> 10 P0010 36 female TRUE TRUE
#> 11 P0011 37 female TRUE TRUE
#> 12 P0012 33 male TRUE FALSE
#> 13 P0013 39 female TRUE TRUE
#> 14 P0014 39 male TRUE TRUE
#> 15 P0015 18 female TRUE TRUE
#> 16 P0016 58 female FALSE FALSE
#> 17 P0017 49 female TRUE TRUE
#> 18 P0018 45 female TRUE TRUE
#> 19 P0019 32 female TRUE TRUE
#> 20 P0020 50 male TRUE TRUE
#> 21 P0021 38 male TRUE TRUE
#> 22 P0022 40 female TRUE TRUE
#> 23 P0023 25 male TRUE TRUE
#> 24 P0024 60 female TRUE TRUE
#> 25 P0025 43 female TRUE TRUE
#> 26 P0026 65 male TRUE TRUE
#> 27 P0027 63 male TRUE TRUE
#> 28 P0028 43 female TRUE TRUE
#> 29 P0029 50 female TRUE TRUE
#> 30 P0030 63 male FALSE FALSE
#> 31 P0031 36 male TRUE TRUE
#> 32 P0032 44 male TRUE TRUE
#> 33 P0033 39 male TRUE TRUE
#> 34 P0034 46 male TRUE FALSE
#> 35 P0035 44 female TRUE TRUE
#> 36 P0036 56 male TRUE TRUE
#> 37 P0037 49 male TRUE TRUE
#> 38 P0038 31 female FALSE FALSE
#> 39 P0039 49 female TRUE TRUE
#> 40 P0040 32 female TRUE TRUE
#> 41 P0041 28 female TRUE TRUE
#> 42 P0042 51 female TRUE TRUE
#> 43 P0043 28 female TRUE TRUE
#> 44 P0044 46 female FALSE FALSE
#> 45 P0045 32 female TRUE TRUE
#> 46 P0046 59 male TRUE TRUE
#> 47 P0047 51 male TRUE TRUE
#> 48 P0048 37 female TRUE TRUE
#> 49 P0049 52 male FALSE FALSE
#> 50 P0050 47 female TRUE TRUE
#> symptom_5_6_7_11_15_20 symptom_5_6_7_13_15_16
#> 1 TRUE TRUE
#> 2 TRUE TRUE
#> 3 TRUE TRUE
#> 4 TRUE TRUE
#> 5 TRUE TRUE
#> 6 TRUE TRUE
#> 7 TRUE TRUE
#> 8 TRUE TRUE
#> 9 TRUE TRUE
#> 10 TRUE TRUE
#> 11 TRUE FALSE
#> 12 FALSE TRUE
#> 13 TRUE TRUE
#> 14 TRUE TRUE
#> 15 TRUE TRUE
#> 16 FALSE FALSE
#> 17 TRUE TRUE
#> 18 TRUE TRUE
#> 19 TRUE TRUE
#> 20 TRUE TRUE
#> 21 TRUE TRUE
#> 22 TRUE TRUE
#> 23 TRUE TRUE
#> 24 TRUE TRUE
#> 25 TRUE TRUE
#> 26 TRUE TRUE
#> 27 TRUE TRUE
#> 28 TRUE TRUE
#> 29 TRUE TRUE
#> 30 FALSE FALSE
#> 31 TRUE TRUE
#> 32 TRUE TRUE
#> 33 TRUE TRUE
#> 34 FALSE FALSE
#> 35 TRUE TRUE
#> 36 TRUE TRUE
#> 37 TRUE TRUE
#> 38 FALSE FALSE
#> 39 TRUE TRUE
#> 40 TRUE TRUE
#> 41 TRUE TRUE
#> 42 TRUE TRUE
#> 43 TRUE TRUE
#> 44 FALSE FALSE
#> 45 TRUE TRUE
#> 46 TRUE TRUE
#> 47 TRUE TRUE
#> 48 TRUE TRUE
#> 49 FALSE FALSE
#> 50 TRUE TRUE
# View summary statistics
results$summary
#> Scenario combination_id rank Total Diagnosed
#> 1 PTSD_orig <NA> NA 45 (90%)
#> 2 symptom_5_6_7_11_15_16 5_6_7_11_15_16 1 43 (86%)
#> 3 symptom_5_6_7_11_15_20 5_6_7_11_15_20 2 43 (86%)
#> 4 symptom_5_6_7_13_15_16 5_6_7_13_15_16 3 43 (86%)
#> Total Non-Diagnosed True Positive True Negative Newly Diagnosed
#> 1 5 (10%) 45 5 0
#> 2 7 (14%) 43 5 0
#> 3 7 (14%) 43 5 0
#> 4 7 (14%) 43 5 0
#> Newly Non-Diagnosed True Cases False Cases Sensitivity Specificity PPV NPV
#> 1 0 50 0 1.0000 1 1 1.0000
#> 2 2 48 2 0.9556 1 1 0.7143
#> 3 2 48 2 0.9556 1 1 0.7143
#> 4 2 48 2 0.9556 1 1 0.7143
#> Accuracy Balanced Accuracy
#> 1 1.00 1.0000
#> 2 0.96 0.9778
#> 3 0.96 0.9778
#> 4 0.96 0.9778
# }