Table 3.
Prediction accuracy of 4- and 6-marker models
| Variable | 1st sample set (CON n = 30, SCZ n = 30) | 2nd sample set (CON n = 30, SCZ n = 30) | Combined (CON n = 60, SCZ n = 60) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| OR | P-valuea | Prediction accuracyb | OR | P-value | Prediction accuracy | OR | P-value | Prediction accuracy | |
| 4-marker model | |||||||||
| MX1 | 11.91 | 0.017 | 26.57 | 0.016 | 19.46 | <0.001 | |||
| GLRX3 | 0.06 | 0.036 | 81.7% | 4.99 | 0.507 | 73.3% | 0.46 | 0.384 | 77.5% |
| UROD | 0.11 | 0.011 | (AUC = 0.86) | 0.55 | 0.609 | (AUC = 0.72) | 0.44 | 0.108 | (AUC = 0.82) |
| GART | 0.02 | 0.006 | 0.01 | 0.006 | 0.01 | <0.001 | |||
| 6-marker model | |||||||||
| MX1 | 7.68 | 0.079 | 16.72 | 0.049 | 20.48 | <0.001 | |||
| GLRX3 | 0.04 | 0.03 | 2.37 | 0.741 | 0.48 | 0.431 | |||
| UROD | 0.07 | 0.012 | 81.7% | 0.79 | 0.875 | 78.3% | 0.51 | 0.235 | 77.5% |
| GART | 0.01 | 0.011 | (AUC = 0.88) | 0.003 | 0.008 | (AUC = 0.66) | 0.01 | <0.001 | (AUC = 0.82) |
| MAPRE1 | 0.19 | 0.095 | 103.8 | 0.052 | 0.73 | 0.629 | |||
| TBCB | 0.13 | 0.050 | 1.93 | 0.616 | 0.42 | 0.138 | |||
OR odds ratio, Combined combined results of 1st sample set and 2nd sample set, AUC the area under the receiver operating characteristic curves
aP-value: multivariate logistic regression analysis
bPrediction accuracy: [1—overall misclassification rate (OMR)] × 100 (%)