Casual Mediation in Non-Randomized and Multilevel Intervention Research

中介模型在药物滥用和艾滋病毒预防和治疗研究中很常见。调解 当自变量（例如物质使用预防计划）对 因变量（例如物质使用）通过第三个变量（例如抵抗技能）。中介分析是 在物质使用和艾滋病毒研究中很重要，因为它可以用来测试基础理论 干预措施；作为增加对干预措施如何发挥作用的理解的一种方式；并作为一种方法 探索数据以确定可以为未来干预措施的发展提供信息的调解途径。 评估调解的传统方法可以在独立个体之间进行有效的因果推论 变量和中介变量之间，但不在中介变量和因变量之间，因为 个体不会被随机分配到中介者的级别。尽管因果推理的新方法 基于统计文献中已得出的潜在结果，但尚未翻译 并落实到预防和治疗研究中。该科学项目旨在研究 在物质使用和艾滋病毒中介模型中得出因果推论的新方法的可行性 干预措施，特别是在存在调节变量的情况下。通过模拟研究，新 方法将与传统方法进行比较，以确定它们是否是对传统方法的改进 传统方法。我们还将扩展这些方法来研究非随机干预措施并应用 它们具有多级嵌套结构的数据。然后新方法将应用于辅助数据 青少年随机药物使用预防计划和非随机研究的分析 感染艾滋病毒的女性。新方法将在软件程序和宏中实施，这将是 免费提供。方法和发现最终将为更具成本效益的设计提供信息 通过允许预防和治疗来干预药物滥用和艾滋病毒预防和治疗 科学家在进行中介分析时得出更有效的因果推论。

Mediation models are common in substance use and HIV prevention and treatment research. Mediation occurs when an independent variable (e.g. substance use prevention program) has an effect on the dependent variable (e.g. substance use) through a third variable (e.g. resistance skills). Mediation analysis Is Important In substance use and HIV research because It can be used to test theories underlying Interventions; as a way of increasing understanding about how interventions work; and as a method for exploring data to Identify mediation pathways that can inform the development of future interventions. Traditional methods for assessing mediation can enable valid causal Inferences between the independent variable and the mediator variable but not between the mediator and the dependent variable because individuals are not randomly assigned to levels of the mediator. Although new methods for causal inference based on potential outcomes have been developed in the statistical literature, they have not been translated and Implemented in prevention and treatment research. This scientific project proposes to examine the feasibility of new methods for drawing causal Inferences In mediation models of substance use and HIV Interventions, particularly in the presence of moderating variables. Using simulation studies, the new methods will be compared with the traditional methods to determine whether they are an improvement over the traditional methods. We will also extend these methods to study non-randomized Interventions and apply them to data with a multilevel nested structure. The new methods will then be applied to a secondary data analysis of a randomized substance use prevention program for adolescents and to a non-randomized study of women with HIV. The new methods will be implemented in software programs and macros, which will be made available free of charge. Methods and findings will ultimately inform the design of more cost-effective Interventions for substance use and HIV prevention and treatment by allowing prevention and treatment scientists to draw more valid causal inferences when conducting mediation analyses.