Estimating Mediation Effects in Prevention Studies

估计预防研究中的中介效应

• 批准号: 10401467
• 负责人: David P MacKinnon
• 金额: $ 34.93万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10401467
• 关键词: Address; Applications Grants; Bayesian Method; Behavioral Mechanisms; Big Data; Biological Models; Communication; Complex; Consultations; Data; Data Analyses; Data Collection; Development; Ecological momentary assessment; Educational workshop; Effectiveness; Etiology; Evaluation; Feedback; Funding; Grant; Individual; Individual Differences; Intervention; Investigation; Link; Literature; Machine Learning; Measurement; Measures; Mediating; Mediation; Mediator of activation protein; Meta-Analysis; Methodology; Methods; Modeling; National Institute of Drug Abuse; Outcome; Persons; Prevention; Prevention Research; Prevention program; Principal Investigator; Procedures; Process; Psychometrics; Publications; Randomized; Recommendation; Research; Research Design; Research Methodology; Research Personnel; Residual state; Statistical Data Interpretation; Statistical Methods; Subgroup; Testing; Time; Translating; United States National Institutes of Health; Work; base; computer program; cost; data space; design; dynamic system; improved; innovation; interest; longitudinal design; model design; multilevel analysis; novel strategies; programs; simulation; substance use treatment; theories; therapy design; tool; treatment research; web site

The purpose of this competing continuation grant proposal is to develop, evaluate and apply methodological and statistical procedures to investigate how prevention programs change outcome variables. These mediation analyses assess the link between program effects on the constructs targeted by a prevention program and effects on the outcome. As noted by many researchers and federal agencies, mediation analyses identify the most effective program components and increase understanding of the underlying mechanisms leading to changing outcome variables. Information from mediation analysis can make interventions more powerful, more efficient, and shorter. The P. I. of this grant received a one-year NIDA small grant and four multi-year grants to develop and evaluate mediation analysis in prevention research. This work led to many publications and innovations. The proposed five-year continuation focuses on the further development and refinement of exciting new mediation analysis statistical developments. Four statistical topics represent next steps in this research and include analytical and simulation research as well as applications to etiological and prevention data. The work expands on our development of causal mediation and Bayesian mediation methods that hold great promise for mediation analysis. In Study 1, practical causal mediation and Bayesian mediation analyses for research designs are developed and evaluated. This approach will clarify methods and develop approaches for dealing with violation of testable and untestable assumptions. Study 2 investigates important measurement issues for the investigation of mediation. This work will focus on methods to identify critical facets of mediating variables, approaches to understanding whether mediators and outcomes are redundant, and develop methods for studies with big data. Study 3 continues the development and evaluation of new longitudinal mediation methods for ecological momentary assessment data and other studies with massive data collection. These new methods promise to more accurately model change over time for both individuals and groups of individuals. Study 4 develops methods to uncover subgroups in mediation analysis including causal mediation methods, multilevel models, and new approaches based on residuals for identifying individuals for whom mediating processes differ in effectiveness from other individuals. For each study, we will investigate unique issues with mediation analysis of prevention data including methods for small N and also massive data collection (big data), the RcErLitEicVaANl rCoEle(Soeef imnsetruacstiounrse):ment for mediating mechanisms, and the application of the growing literature on causal methods and Bayesian methods. Study 5 applies new statistical methods to data from several NIH The project further develops a method, statistical mediation analysis, that extracts more information from funded prevention studies providing important feedback about the usefulness of the methods. Study 6 research. Mediation analysis explains how and why prevention and treatments are successful. Mediation disseminates new information about mediation analysis through our website and other media, by analysis improves prevention and treatment so that their effects are greater and even cost less. communication with researchers, and publications from the project.

这一竞争性连续补助金提案的目的是开发、评估和应用 调查预防项目如何改变结果的方法和统计程序 变量这些中介分析评估了项目对目标结构的影响之间的联系 通过预防计划和对结果的影响。正如许多研究人员和联邦 调解分析确定了最有效的方案组成部分， 理解导致结果变量变化的潜在机制。信息从 调解分析可以使干预更有力、更有效和更短。私家侦探。本 一名赠款人获得了一笔为期一年的NIDA小额赠款和四笔多年赠款，用于开发和评估调解 预防研究分析。这项工作导致了许多出版物和创新。拟议 为期五年的持续工作重点是进一步发展和完善令人兴奋的新调解 分析统计发展。四个统计主题代表了本研究的后续步骤，包括 分析和模拟研究以及应用于病因学和预防数据。工作 扩展了我们的因果调解和贝叶斯调解方法的发展， 调解分析。在研究1中，实际的因果中介和贝叶斯中介分析 为研究设计开发和评估。这种方法将澄清方法， 处理违反可检验和不可检验假设的方法。研究2调查 调解调查的重要衡量问题。这项工作将侧重于方法， 确定调解变量的关键方面，了解调解人是否 结果是多余的，并开发大数据研究的方法。研究3继续 生态瞬时评估新的纵向调解方法的发展和评价 数据和其他大量数据收集的研究。这些新方法有望更准确地 随着时间的推移，个人和个人群体的模型都发生了变化。研究4开发了方法， 揭示中介分析中的子组，包括因果中介方法，多层次模型和新的 基于残差的方法，用于识别调解过程不同的个人， 其他人的有效性。对于每项研究，我们将调查调解的独特问题 分析预防数据，包括小N的方法以及大量数据收集（大数据）， RcErLitEicVaANl rCoEle（Soeef imnsetruacstiounrse）：介导机制的作用，以及越来越多的文献在 因果方法和贝叶斯方法。研究5将新的统计方法应用于来自几个NIH的数据 该项目进一步开发了一种方法，即统计中介分析， 资助的预防研究提供了关于方法有用性的重要反馈。研究6 research.中介分析解释了预防和治疗如何以及为什么成功。调解 通过我们的网站和其他媒体传播有关调解分析的新信息， 分析可改进预防和治疗，从而提高效果，甚至降低成本。 与研究人员的沟通，以及项目的出版物。