Individual growth curve analysis illuminates stability and change in personality disorder features - The longitudinal study of personality disorders
Individual growth curve analysis illuminates stability and change in personality disorder features - The longitudinal study of personality disorders
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DOI：
10.1001/archpsyc.61.10.1015
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发表时间：
2004-10-01
影响因子：
--
通讯作者：
Willett, JB
中科院分区：
文献类型：
--
作者：
Lenzenweger, MF;Johnson, MD;Willett, JB
背景：人格病理学的长期稳定性仍然是一个悬而未决的问题。它的解决方案将来自前瞻性，多波纵向研究，使用人格障碍（PD）的盲法评估。多波数据的信息分析需要应用统计程序，例如个体生长曲线建模，其可以适当地检测和描述个体随时间的变化。人格障碍的纵向研究，这符合当代的方法学设计标准，提供了PD的稳定性和变化从个人的增长曲线perspectives.Methods的调查数据：250名受试者进行了检查，PD功能在3个不同的时间点，使用国际人格障碍考试在4年的研究。随着时间的推移，PD功能的稳定性和变化进行了检查，使用个人的增长modeling.Results：拟合的无条件的增长模型表明，在PD功能存在统计学显着的变化，随着时间的推移，在海拔和个人PD增长轨迹的变化率。其他条件性生长模型的拟合（其中通过受试者的研究组成员资格（无PD vs可能的PD）、性别和入组研究时的年龄预测个体生长曲线的升高和变化率参数）显示，研究组成员资格预测个体生长曲线的升高和变化率。共病轴I的精神病理学和治疗在研究期间与海拔的个人的增长轨迹，但不改变rates of change.Conclusions：从个人的生长曲线分析的角度来看，PD功能显示相当大的变异性，随着时间的推移，在个人之间。这种对个体生长轨迹的细粒度分析提供了PD特征随时间变化的令人信服的证据，并且不支持PD特征随时间推移具有特质样、持久和稳定性的假设。
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 trait-like, enduring, and stable over time.