Casual Mediation in Non-Randomized and Multilevel Intervention Research

非随机和多层次干预研究中的随意调解

基本信息

批准号：
8002610
负责人：
Donna Lynn Coffman
金额：
$ 14.82万
依托单位：
PENNSYLVANIA STATE UNIVERSITY, THE
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/8002610
关键词：
AIDS prevention Accounting Address Adolescent Advanced Development Affect Alcohol or Other Drugs use Characteristics Charge Computer software Data Data Analyses Data Set Development Drug resistance Educational workshop Effectiveness Ensure Equation Event Expectancy Funding Future Gender HIV Hand Hybrids Individual Intervention Intervention Studies Literature Mediating Mediation Mediator of activation protein Mentors Methodology Methods Modeling Outcome Pathway interactions Play Postdoctoral Fellow Prevention Prevention Research Prevention program Procedures Process Publications Randomized Research Research Methodology Research Personnel Resistance Resources Role Schools Science Scientist Statistical Methods Stratification Structure Subgroup Technology Transfer Testing Time Training Translating Treatment outcome Woman Work base cost data structure design effective intervention graduate student improved innovation interest longitudinal analysis programs response simulation skills software development statistics substance use prevention symposium theories treatment program

项目摘要

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.

调解模型在药物使用和HIV预防和治疗研究中很常见。调解当独立变量（例如，预防药物使用程序）对通过第三个变量（例如，电阻技能）（例如，使用物质使用）的因变量（例如使用物质）。调解分析是在物质使用和艾滋病毒研究中很重要，因为它可用于测试基础理论干预措施；作为一种对干预措施如何运作的理解的一种方式；作为一种方法探索数据以识别可以告知未来干预措施发展的调解途径。评估调解的传统方法可以实现独立之间有效的因果推断变量和调解器变量，但不在调解器和因变量之间，因为个人未随机分配到调解人的水平。虽然因果推断的新方法基于统计文献中的潜在结果，它们尚未翻译并在预防和治疗研究中实施。这个科学项目建议检查在物质使用和艾滋病毒中调解模型中绘制因果推断的新方法的可行性干预措施，特别是在有调节变量的情况下。使用仿真研究，新的将方法与传统方法进行比较，以确定它们是否有所改进传统方法。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可以使用多级嵌套结构的数据。然后，新方法将应用于辅助数据分析针对青少年的随机物质使用预防计划和非随机研究艾滋病毒的妇女。新方法将在软件程序和宏中实现，这将是免费提供。方法和发现最终将为设计提供更具成本效益的设计通过允许预防和治疗来进行药物使用和艾滋病毒预防和治疗的干预措施进行调解分析时，科学家会提取更有效的因果推断。