The model predictions were then used to predict illegal behavior including arrests. The fitted values of the final model were averaged across time and then across the multiple imputations. Therefore, the final fitted values represented the average level of predicted externalizing problems from ages 5–27. We only examined the predictions in relation to the observed values of the outcomes (the illegal behaviors were not imputed) to avoid overestimating the model’s predictive ability. In ROC curves, the area under the curve (AUC) represents the probability that a randomly selected person meeting the diagnostic threshold (i.e., having been arrested) will have a higher test result (i.e., more externalizing problems) than a randomly selected person who does not meet the cutoff. The AUC represents the tradeoff between a test’s sensitivity and specificity. Sensitivity is the likelihood of correctly identifying individuals meeting the diagnostic threshold (true positive rate or hits). Specificity is the likelihood of correctly identifying individuals not meeting the diagnostic threshold (true negative rate or correct rejections). In general, a higher AUC, sensitivity, and specificity represent a better performing diagnostic test (range: 0–1, chance = 0.5).
