Casual Mediation in Non-Randomized and Multilevel Intervention Research

调解模式在药物使用和艾滋病毒预防和治疗研究中很常见。调解 当一个独立变量（例如物质使用预防计划）对 从因变量（如物质使用）到第三个变量（如抵抗技能）。调解分析是 重要的物质使用和艾滋病毒研究，因为它可以用来测试理论基础 干预措施;作为提高对干预措施如何运作的理解的一种方式;并作为一种方法， 探索数据，以确定可为未来干预措施的制定提供信息的调解途径。 评估中介的传统方法可以在独立的 变量和中介变量之间，而不是中介变量和因变量之间，因为 个人不是随机分配到调解人的级别。虽然因果推理的新方法 根据统计文献中的潜在结果，尚未翻译 并应用于预防和治疗研究。这个科学项目建议检查 在物质使用和艾滋病毒的中介模型中进行因果推断的新方法的可行性 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，以确定它们是否优于 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 将它们转换为具有多级嵌套结构的数据。然后将新方法应用于辅助数据 一项青少年药物滥用预防计划的随机分析和一项非随机研究 感染艾滋病毒的女性。新方法将在软件程序和宏中实现， 免费提供。方法和研究结果将最终通知设计更具成本效益 通过允许预防和治疗，对药物使用和艾滋病毒预防和治疗进行干预 科学家在进行调解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.