The clinical course of alcohol use disorders: Using joinpoint analysis to aid in interpretation of growth mixture models
The clinical course of alcohol use disorders: Using joinpoint analysis to aid in interpretation of growth mixture models
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DOI：
10.1016/j.drugalcdep.2013.06.033
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发表时间：
2013-12-01
影响因子：
4.2
通讯作者：
Maisto, Stephen A.
中科院分区：
文献类型：
--
作者：
Prince, Mark A.;Maisto, Stephen A.
背景：酒精使用障碍(AUD)的临床病程具有个体内和个体间的高度异质性。对这种异质性建模的一种方法是潜在生长混合建模(LGMM)，它识别一些潜在的饮酒者亚群，其饮酒轨迹在潜在亚群内相似，但在亚群之间不同。LGMM是数据驱动的，使用一个迭代过程来测试研究人员选择的潜伏子组的序号，然后选择最佳拟合模型。尽管LGMM有优势(例如，在不同的纵向数据中识别亚组)，但LGMM的一个限制是缺乏精确度，无法模拟临床医生经常观察到的治疗期间饮酒的突然变化。JPA是一种数据分析程序，用于识别纵向数据中的离散变化点(例如，从增加到减少或从减少到增加的变化)。方法：本研究展示了使用JPA作为LGMM的后处理程序来提高AUD临床病程突变建模的准确性。结果：参与NIAAA赞助的复发复制和扩展项目的549名AUD参与者的二次数据分析结果揭示了四种潜在的饮酒轨迹。讨论：在这些轨迹中，JPA的加入提高了AUDS临床病程建模的精确度。(C)2013爱思唯尔爱尔兰有限公司。保留所有权利。
Background: The clinical course of alcohol use disorders (AUD) is marked by great heterogeneity both within and between individuals. One approach to modeling this heterogeneity is latent growth mixture modeling (LGMM), which identifies a number of latent subgroups of drinkers with drinking trajectories that are similar within a latent subgroup but different between subgroups. LGMM is data-driven and uses an iterative process of testing a sequential number researcher-selected of latent subgroups then selecting the best fitting model. Despite the advantages of LGMM (e.g., identifying subgroups among heterogeneous longitudinal data), one limitation is the lack of precision of LGMM to model abrupt changes in drinking during treatment that are often observed by clinicians. Joinpoint analysis (JPA) is a data analysis procedure that is used to identify discrete change points in longitudinal data (e.g., changes from increasing to decreasing or decreasing to increasing).Method: This study presents a demonstration of using JPA as a post hoc procedure for LGMM to improve accuracy in modeling abrupt changes in clinical course of AUD.Results: Results from this secondary data analysis of 549 AUD participants participating in the NIAAA sponsored relapse replication and extension project uncovered four latent classes of drinking trajectories.Discussion: Within these trajectories the addition of JPA improved precision in modeling the clinical course of AUDs. (C) 2013 Elsevier Ireland Ltd. All rights reserved.