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Table 3 Characteristics of SIF Predictive Tool

From: Developing a predictive tool for psychological well-being among Chinese adolescents in the presence of missing data

  Training Samples Validation Samples
Measure Estimate (SE) 95% CI Estimate (SE) 95% CI
AUC 0.758 (0.017) 0.722 to 0.794 0.755 (0.014) 0.726 to 0.783
Calibration Slopeª 0.993 (0.076) 0.838 to 1.147 0.976 (0.059) 0.858 to 1.094
Sensitivityb 0.478 (0.030) 0.417 to 0.540 0.480 (0.025) 0.428 to 0.531
Specificityb 0.862 (0.008) 0.848 to 0.877 0.860 (0.007) 0.845 to 0.874
Positive Predictive Valueb 0.441 (0.022) 0.397 to 0.485 0.431 (0.022) 0.388 to 0.473
Negative Predictive Valueb 0.879 (0.011) 0.856 to 0.903 0.882 (0.008) 0.865 to 0.898
Likelihood Ratio of positive testb 3.478 2.879 to 4.200 3.414 2.918 to 3.993
Likelihood Ratio of negative testb 0.604 0.534 to 0.684 0.605 0.547 to 0.670
Diagnostic Odds Ratiob, c 5.755 4.241 to 7.810 5.638 4.390 to 7.241
  1. ªCalibration slope is defined as the regression coefficient in the logistic regression of psychosocial maladjustment on the SIF Predictive Tool.
  2. bSensitivity, specificity, positive and negative predictive values, likelihood ratios of a positive and negative test, and the diagnostic odds ratio assume that a negative test is defined as when the SIF Predictive Tool score exceeds 1.
  3. cDiagnostic odds ratio equals the likelihood ratio of a positive test divided by the likelihood ratio of a negative test.
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