RANDOM EFFECT MODELS FOR SUBSTANCE ABUSE RESEARCH

描述：(申请人摘要) 这是用于二次数据分析，它将应用和测试 最新的多水平统计方法在中国的应用 预防药物滥用。要测试的分析技术， 随机效应模型(REM)允许同时处理来自 不同级别(例如，学生、班级、学校等)。传统型 单水平分析方法和传统的多水平模型 在指定实质性模型或统计数据方面存在缺陷 测试假设，这反过来可能导致不可靠的结果和 错误的结论。REM提供了一个更合适的机制来 代表了多层次数据的复杂性和更完善的模型 测试社交环境的影响的规范。测试的能力 语境效应使REM的应用成为一种非常有前途的选择 用于药物滥用研究中的数据分析。具有灵活性，以 除了模型随机群体效应，REM还提供了一个更强大的选择 考察社会环境对物质使用的影响。的扩展 这种药物滥用研究的方法可能有助于我们更多地了解 预防计划的影响。 在这个拟议的项目中，将开发分层线性模型并 应用于药物滥用预防研究的数据，以确定 对物质使用产生重要的背景和个人影响。结果 将从REM获得的结果与其他常规方法的结果进行比较 多层次的方法。此外，对违规行为的稳健性问题 正态分布和对REM组数的敏感性 将会被调查。其他潜在的方法论问题，例如 二分法的结果、潜在因素和生存分析也将是 检查过了。这个项目的发现应该为以下方面提供指导 适当应用REM，更好地理解REM的贡献 环境背景对预防方案的影响，并且更有效 对药物滥用的规划效果的评估。

DESCRIPTION: (Applicant's Abstract) This is for secondary data analysis that will apply and test a state-of-the-art multilevel statistical approach to research on the prevention of substance abuse. The analytical technique to be tested, the random-effect model (REM) allows simultaneous processing of information from different levels (e.g., student, class, school, etc.). Conventional approaches with single-level analyses and conventional multilevel models contain shortcomings in specifying substantive models or in statistical testing of hypotheses, which in turn may lead to unreliable results and erroneous conclusions. The REM provides a more appropriate mechanism to represent the complexity of the multilevel data and a more adequate model specification to test the impact of social contexts. The ability to test contextual effects makes the application of REM a very promising alternative for data analyses in substance abuse research. Having the flexibility to model random group effects, the REM also offers a more powerful alternative to examine the impact of social context on substance use. The extension of this approach to substance abuse research may help us understand more about the impact of prevention programs. In this proposed project, hierarchical linear models will be developed and applied to data from a substance abuse prevention study to identify important contextual and individual effects on substance use. Results obtained from the REM will be compared to that of other conventional multilevel approaches. In addition, the issues of robustness to violations of normal distribution and sensitivity to the number of groups of the REM will be investigated. Other potential methodological concerns, such as dichotomized outcome, latent factor, and survival analysis, will also be examined. Findings from this project should provide guidelines for appropriate REM application, a better understanding of the contribution of environmental contexts to effects of prevention programs, and more valid estimation of program effects on substance abuse.