BACKGROUND: The long-term stability of personality pathology remains an open question. Its resolution will come from prospective, multiwave longitudinal studies using blinded assessments of personality disorders (PD). Informative analysis of multiwave data requires the application of statistical procedures, such as individual growth curve modeling, that can detect and describe individual change appropriately over time. The Longitudinal Study of Personality Disorders, which meets contemporary methodological design criteria, provides the data for this investigation of PD stability and change from an individual growth curve perspective. METHODS: Two hundred fifty subjects were examined for PD features at 3 different time points using the International Personality Disorders Examination during a 4-year study. Stability and change in PD features over time were examined using individual growth modeling. RESULTS: Fitting of unconditional growth models indicated that statistically significant variation in PD features existed across time in the elevation and rate of change of the individual PD growth trajectories. Fitting of additional conditional growth models, in which the individual elevation and rate-of-change growth parameters were predicted by subjects' study group membership (no PD vs possible PD), sex, and age at entry into the study, showed that study group membership predicted the elevation and rate of change of the individual growth curves. Comorbid Axis I psychopathology and treatment during the study period were related to elevations of the individual growth trajectories, but not to rates of change. CONCLUSIONS: From the perspective of individual growth curve analysis, PD features show considerable variability across individuals over time. This fine-grained analysis of individual growth trajectories provides compelling evidence of change in PD features over time and does not support the assumption that PD features are traitlike, enduring, and stable over time.
BACKGROUND: The long-term stability of personality pathology remains an open question. Its resolution will come from prospective, multiwave longitudinal studies using blinded assessments of personality disorders (PD). Informative analysis of multiwave data requires the application of statistical procedures, such as individual growth curve modeling, that can detect and describe individual change appropriately over time. The Longitudinal Study of Personality Disorders, which meets contemporary methodological design criteria, provides the data for this investigation of PD stability and change from an individual growth curve perspective. METHODS: Two hundred fifty subjects were examined for PD features at 3 different time points using the International Personality Disorders Examination during a 4-year study. Stability and change in PD features over time were examined using individual growth modeling. RESULTS: Fitting of unconditional growth models indicated that statistically significant variation in PD features existed across time in the elevation and rate of change of the individual PD growth trajectories. Fitting of additional conditional growth models, in which the individual elevation and rate-of-change growth parameters were predicted by subjects' study group membership (no PD vs possible PD), sex, and age at entry into the study, showed that study group membership predicted the elevation and rate of change of the individual growth curves. Comorbid Axis I psychopathology and treatment during the study period were related to elevations of the individual growth trajectories, but not to rates of change. CONCLUSIONS: From the perspective of individual growth curve analysis, PD features show considerable variability across individuals over time. This fine-grained analysis of individual growth trajectories provides compelling evidence of change in PD features over time and does not support the assumption that PD features are traitlike, enduring, and stable over time.
Authors: Leslie C Morey; M Tracie Shea; John C Markowitz; Robert L Stout; Christopher J Hopwood; John G Gunderson; Carlos M Grilo; Thomas H McGlashan; Shirley Yen; Charles A Sanislow; Andrew E Skodol Journal: Am J Psychiatry Date: 2010-02-16 Impact factor: 18.112
Authors: Nikolai Czajkowski; Steven H Aggen; Robert F Krueger; Kenneth S Kendler; Michael C Neale; Gun Peggy Knudsen; Nathan A Gillespie; Espen Røysamb; Kristian Tambs; Ted Reichborn-Kjennerud Journal: Am J Psychiatry Date: 2018-03-21 Impact factor: 18.112