MULTILEVEL MODELING OF DRUG USE TRAJECTORIES

药物使用轨迹的多层次建模

• 批准号: 2377411
• 负责人: RICHARD R CLAYTON
• 金额: $ 8.33万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-03-01 至 1999-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2377411
• 关键词: adolescence (12-20); behavior prediction; behavioral /social science research tag; clinical research; drug abuse; epidemiology; family structure /dynamics; human data; longitudinal human study; model design /development; peer group; psychological models; social support network; socioenvironment; sociometry; statistics /biometry; young adult human (21-34)

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.

该申请提出了二级数据分析，并 三个纵向样本的态度：列克星敦纵向 研究，全国青年纵向调查（NLSY）主要样本， NLSY儿童样本。 物质使用的纵向分析 常规方法的局限性通常受到阻碍。 需要统计程序，可以在 个人级别，利用来自所有测量浪潮的数据，包括 属性，评估固定和时变的协变量的影响 关于吸毒的发育途径，并建模 上下文因素。利用最近开发的技术，这 研究将将三阶段混合效应回归模型应用于 表征和预测连续药物的个体水平变化 这些次要数据的结果。 广义线性混合模型 提出了分析一系列离散药物结果的程序。 建议的分析在数据集中选择性分发，具体取决于 关于适当措施的可用性。分析计划有六个 相互依存的组件。首先，分析将使用无条件的随机 效果回归模型以估计和表征个体 药物使用轨迹和药物相关态度的差异 多个测量波。其次，分析将使用两级混合 影响回归模型，以预测药物使用中的个体变化和 与药物相关的态度途径是固定和时间的函数 人的特征不同，并评估倒数 不同药物结局变化率之间的关系。第三， 分析将使用三级混合效应回归模型进行估计 并表征个人级别的关系变化之间的关系 预测因素和药物结果是多种上下文的函数 设置（家庭，学校，社区和同伴网络）。 第四， 分析将使用混合效应模型来汇总重叠的数据 并评估并评估建模物质的可行性 以这种方式超越了扩展发展序列的轨迹。 第五，探索性分析将评估随机跨的影响 将主题分类为多种情况。第六，探索性 分析将使用潜在可变增长模型来建模 药物使用的测量误差。拟议的研究将有助于 了解青年和早期吸毒的发育途径 成年。该研究也可能有助于多个政策维度 在药物研究中，包括预防，干预和 治疗策略对个体差异有更大的认识 随着时间的流逝，吸毒的途径；增强对 个人水平风险和保护因素的后果 吸毒动力；以及家庭和社区层面的发展 预防和干预计划以及此类计划的整合 采用个人级别的方法。