Find optimal non-hierarchical six-symptom combinations for PTSD diagnosis
Source:R/analysis.R
analyze_best_six_symptoms_four_required.Rd`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.
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:
Tests all possible combinations of 6 symptoms from the 20 PCL-5 items
Requires 4 symptoms to be present (>=2 on original 0-4 scale) for diagnosis
Identifies the three combinations that best match the original DSM-5 diagnosis
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)
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
# Use a 250-row subset of the bundled data to keep the example fast
ptsd_data <- rename_ptsd_columns(simulated_ptsd[1:250, ],
id_col = c("patient_id", "age", "sex"))
# \donttest{
# Find best combinations with the default balanced-accuracy criterion
results <- analyze_best_six_symptoms_four_required(ptsd_data)
#> Warning: `analyze_best_six_symptoms_four_required()` was deprecated in PTSDdiag 0.2.1.
#> ℹ Please use `optimize_combinations()` instead.
#> Evaluating combinations ■■■■■■ 17% | ETA: 5s
#> Evaluating combinations ■■■■■■■■■■■■■■■■■ 53% | ETA: 3s
#> Evaluating combinations ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 100% | ETA: 0s
#> ℹ Evaluated 38760 combinations. Best: 6, 7, 8, 11, 13, 17 (1 additional tied)
# Get symptom numbers
results$best_symptoms
#> [[1]]
#> [1] 6 7 8 11 13 17
#>
#> [[2]]
#> [1] 6 7 10 11 13 15
#>
#> [[3]]
#> [1] 4 6 7 8 11 17
#>
# View raw comparison data
results$diagnosis_comparison
#> patient_id age sex PTSD_orig symptom_6_7_8_11_13_17
#> 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 FALSE
#> 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 TRUE
#> 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 TRUE
#> 35 P0035 44 female TRUE TRUE
#> 36 P0036 56 male TRUE TRUE
#> 37 P0037 49 male TRUE TRUE
#> 38 P0038 31 female FALSE TRUE
#> 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
#> 51 P0051 28 female TRUE TRUE
#> 52 P0052 40 female TRUE TRUE
#> 53 P0053 31 male TRUE TRUE
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#> 55 P0055 34 male TRUE TRUE
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#> 58 P0058 30 female TRUE TRUE
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#> 60 P0060 30 female TRUE TRUE
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#> 65 P0065 21 male TRUE TRUE
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#> 68 P0068 56 male TRUE TRUE
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#> 70 P0070 44 female FALSE FALSE
#> 71 P0071 48 male TRUE TRUE
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#> 73 P0073 58 female TRUE TRUE
#> 74 P0074 32 male TRUE TRUE
#> 75 P0075 47 female TRUE TRUE
#> 76 P0076 30 male FALSE FALSE
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#> 80 P0080 51 male TRUE TRUE
#> 81 P0081 28 male TRUE TRUE
#> 82 P0082 60 female TRUE TRUE
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#> 84 P0084 23 male TRUE TRUE
#> 85 P0085 41 male TRUE TRUE
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#> 90 P0090 71 female TRUE TRUE
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#> 92 P0092 60 male TRUE TRUE
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#> 96 P0096 39 female TRUE TRUE
#> 97 P0097 30 male FALSE FALSE
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#> 100 P0100 46 male TRUE TRUE
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#> 108 P0108 60 male TRUE TRUE
#> 109 P0109 28 male TRUE TRUE
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#> 111 P0111 24 male TRUE TRUE
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#> 119 P0119 69 male TRUE TRUE
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#> 123 P0123 45 male TRUE TRUE
#> 124 P0124 53 female TRUE TRUE
#> 125 P0125 21 male TRUE TRUE
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#> 149 P0149 45 male FALSE FALSE
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#> 160 P0160 46 male TRUE TRUE
#> 161 P0161 59 female FALSE FALSE
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#> 163 P0163 32 male TRUE TRUE
#> 164 P0164 51 female FALSE FALSE
#> 165 P0165 37 female TRUE TRUE
#> 166 P0166 39 male TRUE TRUE
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#> 173 P0173 53 female TRUE TRUE
#> 174 P0174 36 female FALSE FALSE
#> 175 P0175 43 male TRUE TRUE
#> 176 P0176 44 female TRUE TRUE
#> 177 P0177 20 female TRUE TRUE
#> 178 P0178 46 female TRUE TRUE
#> 179 P0179 60 male TRUE TRUE
#> 180 P0180 42 female TRUE TRUE
#> 181 P0181 33 male TRUE TRUE
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#> 183 P0183 45 female TRUE TRUE
#> 184 P0184 54 male TRUE TRUE
#> 185 P0185 45 female TRUE TRUE
#> 186 P0186 49 male TRUE TRUE
#> 187 P0187 59 male TRUE TRUE
#> 188 P0188 50 female TRUE TRUE
#> 189 P0189 47 male TRUE TRUE
#> 190 P0190 34 female TRUE TRUE
#> 191 P0191 49 male TRUE TRUE
#> 192 P0192 37 female TRUE TRUE
#> 193 P0193 68 female TRUE TRUE
#> 194 P0194 46 female TRUE TRUE
#> 195 P0195 32 female TRUE TRUE
#> 196 P0196 62 female TRUE TRUE
#> 197 P0197 36 female TRUE TRUE
#> 198 P0198 23 female TRUE TRUE
#> 199 P0199 29 female TRUE TRUE
#> 200 P0200 42 male TRUE TRUE
#> 201 P0201 37 female FALSE FALSE
#> 202 P0202 48 female TRUE TRUE
#> 203 P0203 39 male TRUE TRUE
#> 204 P0204 20 male TRUE TRUE
#> 205 P0205 39 female TRUE TRUE
#> 206 P0206 63 female TRUE TRUE
#> 207 P0207 37 male TRUE TRUE
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#> 209 P0209 41 male FALSE FALSE
#> 210 P0210 62 female TRUE TRUE
#> 211 P0211 32 male TRUE TRUE
#> 212 P0212 51 male TRUE TRUE
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#> 214 P0214 38 female TRUE TRUE
#> 215 P0215 38 male TRUE TRUE
#> 216 P0216 51 male FALSE FALSE
#> 217 P0217 49 male TRUE TRUE
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#> 219 P0219 55 male TRUE TRUE
#> 220 P0220 68 female TRUE TRUE
#> 221 P0221 43 female TRUE TRUE
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#> 223 P0223 36 female TRUE TRUE
#> 224 P0224 33 male TRUE TRUE
#> 225 P0225 38 male TRUE TRUE
#> 226 P0226 56 male TRUE TRUE
#> 227 P0227 35 male TRUE TRUE
#> 228 P0228 32 male TRUE TRUE
#> 229 P0229 48 female TRUE TRUE
#> 230 P0230 50 male TRUE TRUE
#> 231 P0231 39 male FALSE FALSE
#> 232 P0232 47 female TRUE TRUE
#> 233 P0233 48 female TRUE TRUE
#> 234 P0234 46 female TRUE TRUE
#> 235 P0235 35 male TRUE TRUE
#> 236 P0236 51 female TRUE TRUE
#> 237 P0237 18 female TRUE TRUE
#> 238 P0238 49 male TRUE TRUE
#> 239 P0239 47 female TRUE TRUE
#> 240 P0240 67 female TRUE TRUE
#> 241 P0241 45 female TRUE TRUE
#> 242 P0242 43 male TRUE TRUE
#> 243 P0243 56 male TRUE FALSE
#> 244 P0244 51 male TRUE TRUE
#> 245 P0245 42 male TRUE TRUE
#> 246 P0246 42 male FALSE FALSE
#> 247 P0247 44 female TRUE TRUE
#> 248 P0248 29 female TRUE TRUE
#> 249 P0249 57 male TRUE TRUE
#> 250 P0250 36 male TRUE TRUE
#> symptom_6_7_10_11_13_15 symptom_4_6_7_8_11_17
#> 1 TRUE TRUE
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#> 6 FALSE FALSE
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#> 250 TRUE TRUE
# View summary statistics
results$summary
#> Scenario combination_id rank Total Diagnosed
#> 1 PTSD_orig <NA> NA 232 (92.8%)
#> 2 symptom_6_7_8_11_13_17 6_7_8_11_13_17 1 230 (92%)
#> 3 symptom_6_7_10_11_13_15 6_7_10_11_13_15 2 230 (92%)
#> 4 symptom_4_6_7_8_11_17 4_6_7_8_11_17 3 229 (91.6%)
#> Total Non-Diagnosed True Positive True Negative Newly Diagnosed
#> 1 18 (7.2%) 232 18 0
#> 2 20 (8%) 229 17 1
#> 3 20 (8%) 229 17 1
#> 4 21 (8.4%) 228 17 1
#> Newly Non-Diagnosed True Cases False Cases Sensitivity Specificity PPV
#> 1 0 250 0 1.0000 1.0000 1.0000
#> 2 3 246 4 0.9871 0.9444 0.9957
#> 3 3 246 4 0.9871 0.9444 0.9957
#> 4 4 245 5 0.9828 0.9444 0.9956
#> NPV Accuracy Balanced Accuracy
#> 1 1.0000 1.000 1.0000
#> 2 0.8500 0.984 0.9658
#> 3 0.8500 0.984 0.9658
#> 4 0.8095 0.980 0.9636
# }