Exploring the predictive validity of personality disorder criteria.

Abstract

We tested the predictive validity of personality disorder (PD) indicators at different levels of aggregation, ranging from general PD severity to PD syndrome scales to individual PD criteria. We compared the predictive validity of models on these levels based on interview data on all 78 DSM-IV PD criteria, by using 19 outcome scales in three different samples (N = 651, N = 552, and N = 1,277). We hypothesized that criteria of personality pathology yield a significant increase in predictive validity compared with scales that are aggregated at the syndrome- or general severity-level. We assessed out of sample performance of predictive models in a repeated cross-validation design using regularized linear regression and regression forest algorithms. We observed no significant difference in predictive performance between models trained at the item-level and models trained on scale-level data. We further tested the predictive performance of the trained linear models across samples on outcome measures shared between samples and inspected models for criteria-level information they relied on to make predictions. Our results suggest that little predictive variance is lost when interview items assessing DSM-IV PD criteria are aggregated to dimensional PD scales.