Casual Mediation in Non-Randomized and Multilevel Intervention Research

调解模式在物质使用和艾滋病毒防治研究中很常见。调解 当一个自变量(例如，药物使用预防计划)对 因变量(如物质使用)通过第三个变量(如抵抗力技能)。调解分析是 在物质使用和艾滋病毒研究中很重要，因为它可以用来检验潜在的理论 干预措施；作为增加对干预措施如何工作的了解的一种方式；以及作为一种方法 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。 传统的调解评估方法可以使独立的 变量和中介变量之间，而不是中介变量和因变量之间，因为 个人不会被随机分配到调解人的级别。尽管因果推论的新方法 基于统计文献中已开发的潜在结果，它们尚未被翻译 并在预防和治疗研究中实施。这一科学项目建议研究 药物使用与HIV中介模型中因果推理新方法的可行性 干预措施，特别是在存在调节变量的情况下。利用模拟研究，新的 方法将与传统方法进行比较，以确定它们是否比 传统的方法。我们还将把这些方法扩展到研究非随机干预并应用 将它们转换为具有多层嵌套结构的数据。然后，新方法将应用于辅助数据 一项针对青少年的随机药物使用预防计划和一项非随机研究的分析 艾滋病毒携带者的女性。新方法将在软件程序和宏中实现，这将是 免费提供。方法和结果最终将为设计提供更具成本效益的 通过允许预防和治疗来干预药物使用和艾滋病毒预防和治疗 在进行调解分析时，科学家需要得出更多有效的因果推断。

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.