Table 5.
Post Hoc Binary Logistic Regression
| Model With Predictors | Diagnostic Status (MDD, HC) |
||||
|---|---|---|---|---|---|
| R2 | χ2 | OR (95% CI) | p Value | VIF | |
| Overall Model | 0.72 | 47.4a | – | – | – |
| High Effort P3a | – | – | 0.74 (0.62–0.87) | <.001 | 1.60 |
| Low Effort SPN | – | – | 1.30 (0.94–1.79) | .113 | 1.13 |
| High Effort RewPb | – | – | 0.78 (0.64–0.96) | .021 | 1.97 |
| Ageb | – | – | 0.91 (0.83–0.99) | .022 | 1.70 |
Logistic regression was used to predict depression diagnostic status (0 = HC, 1 = MDD). The Nagelkerke R2 and χ2 statistic are reported for the logistic regression model and reflect statistics comparing the full model to the null model.
HC, healthy control; MDD, major depressive disorder; OR, odds ratio; RewP, reward positivity; SPN, stimulus-preceding negativity; VIF, variance inflation factor.
a
p < .001.
b
p < .05.