协变量对生长混合模型的影响。
Influences of a Covariate on Growth Mixture Modeling.
作者信息
Huang David, Brecht Mary-Lynn, Hara Motoaki, Hser Yih-Ing
机构信息
UCLA Integrated Substance Abuse Programs, Semel Institute for Neuroscience and Human Behavior, 1640 S. Sepulveda Blvd., Suite 200, Los Angeles, CA 90025.
出版信息
J Drug Issues. 2010 Winter;40(1):173-194. doi: 10.1177/002204261004000110.
Abstract
This study investigated the influence of including a covariate and/or a distal outcome on growth mixture modeling (GMM). GMM was used to examine patterns of days of heroin use over 16 years among 471 heroin users and the relationship of those patterns to mortality (distal outcome). Comparisons were made among four types of models: without a covariate and a distal outcome (two-stage approach), with a distal outcome, with a covariate, and with a covariate and a distal outcome in conjunction with three different covariates. The two-stage approach and models with the inclusion of a distal outcome resulted in different conclusions when testing the impact of latent trajectory membership on the distal outcome. Differences in membership classifications between unconditional and conditional models were mainly determined by two factors: (1) the associations of the trajectories with the covariate and the distal outcome, and (2) the distribution of the covariate in the study sample.
摘要
本研究调查了纳入协变量和/或远端结局对生长混合模型（GMM）的影响。GMM用于检验471名海洛因使用者在16年期间的海洛因使用天数模式，以及这些模式与死亡率（远端结局）之间的关系。对四种类型的模型进行了比较：无协变量和远端结局（两阶段法）、有远端结局、有协变量，以及有协变量和远端结局并结合三种不同的协变量。在检验潜在轨迹类别对远端结局的影响时，两阶段法和纳入远端结局的模型得出了不同的结论。无条件模型和条件模型之间类别分类的差异主要由两个因素决定：（1）轨迹与协变量和远端结局的关联，以及（2）协变量在研究样本中的分布。
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