MULTILEVEL MODELING OF DRUG USE TRAJECTORIES

该应用程序提出了药物使用的二级数据分析， 三个纵向样本的态度：列克星敦纵向 研究，国家青年纵向调查（NLSY）的主要样本， NLSY儿童样本。 药物使用的纵向分析有 经常受到传统方法的限制。 需要统计程序来模拟 单个水平，利用来自所有测量波的数据，包括 磨碎机，评估固定和随时间变化的协变量的影响 药物使用的发展途径，并模拟 背景因素利用最近开发的技术， 研究将采用三阶段混合效应回归模型， 表征和预测连续给药中的个体水平变化 这些次要数据的结果。 广义线性混合模型 程序提出了一系列离散的药物结果进行分析。 建议的分析选择性地分布在数据集上，具体取决于 是否有适当的措施。分析计划有六个 相互依存的组成部分。首先，分析将使用无条件随机 影响回归模型来估计和表征个体 毒品使用轨迹和对毒品的态度的差异 多波测量。第二，分析将使用两级混合 影响回归模型预测药物使用的个体差异， 药物相关的态度途径作为固定和时间的函数， 不同的人的特点，并评估互惠 不同药物结果变化率之间的关系。第三、 分析将使用三水平混合效应回归模型来估计 并描述个体水平的差异， 预测因子和药物结果作为多种背景的函数 环境（家庭、学校、社区和同伴网络）。 第四， 分析将使用混合效应模型， 队列，并评估建模物质使用的可行性 以这种方式在扩展的发展序列上的轨迹。 第五，探索性分析将评估随机交叉的影响， 将主题分类到多个上下文中。第六，探索性 分析将使用潜在变量增长模型来模拟 药物使用中的测量误差。拟议的研究将有助于 了解青少年和早期吸毒的发展途径 成年这项研究还可能有助于多个政策层面 在药物研究中，包括预防、干预和 更多地认识到个体差异的治疗策略 在药物使用途径中的作用; 个人水平的风险和保护因素对 药物使用的动态;家庭和社区一级的发展 预防和干预方案以及这些方案的整合 个人层面的方法。

This application proposes secondary data analyses of drug use and attitudes in three longitudinal samples: the Lexington Longitudinal Study, the National Longitudinal Survey of Youth (NLSY) Main sample, and the NLSY Child sample. Longitudinal analyses of substance use have frequently been hampered by the limitations of conventional methods. Statistical procedures are needed which can model change at the individual level, utilize data from all waves of measurement and include attriters, evaluate the effects of both fixed and time-varying covariates on developmental pathways for drug use, and model the influence of contextual factors. Drawing upon recently developed techniques, this study will apply three-stage mixed effects regression models to characterize and predict individual level variation in continuous drug outcomes in these secondary data. Generalized linear mixed model procedures are proposed to analyze series of discrete drug outcomes. Proposed analyses are distributed selectively across datasets, depending on the availability of appropriate measures. The analysis plan has six interdependent components. First, analysis will use unconditional random effects regression models to estimate and characterize individual differences in trajectories of drug use and drug-related attitudes over multiple waves of measurement. Second, analysis will use two-level mixed effects regression models to predict individual variation in drug use and drug-related attitudinal pathways as a function of both fixed and time- varying characteristics of persons, and to evaluate reciprocal relationships between rates of change in different drug outcomes. Third, analysis will use three-level mixed effects regression models to estimate and characterize individual level variation in relationships between predictors and drug outcomes as a function of multiple contextual settings (family, school, neighborhood, and peer network). Fourth, analysis will use mixed effects models to pool data across overlapping cohorts and evaluate the feasibility of modeling substance use trajectories over extended developmental sequences in this fashion. Fifth, exploratory analyses will evaluate the influence of random cross- classification of subjects into multiple contexts. Sixth, exploratory analyses will use latent variable growth models to model the effects of measurement error in drug use. The proposed study will contribute to the understanding of developmental pathways for drug use in youth and early adulthood. The study may also contribute to multiple policy dimensions in drug studies, including the design of prevention, intervention, and treatment strategies with greater recognition of individual differences in pathways of drug use over time; enhanced understanding of the consequences of individual level risk and protective factors on the dynamics of drug use; and the development of family and community level prevention and intervention programs and the integration of such programs with individual level approaches.