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.

这一竞争性续批提案的目的是开发、评估和应用 调查预防方案如何改变结果的方法和统计程序 变量。这些中介分析评估计划对目标构造的影响之间的联系 通过预防计划和对结果的影响。正如许多研究人员和联邦政府所指出的那样 机构、调解分析确定最有效的计划组成部分并增加 了解导致结果变量变化的潜在机制。信息来自 调解分析可以使干预更有力、更有效、更短。这件事的私人侦探 格兰特收到了一笔为期一年的NIDA小额赠款和四笔多年期赠款，用于开发和评估调解 预防研究中的分析。这项工作导致了许多出版物和创新。建议数 五年延续的重点是进一步发展和完善令人振奋的新调解 分析统计动态。四个统计主题代表了这项研究的下一步，包括 分析和模拟研究以及病因学和预防数据的应用。这项工作 扩展了我们对因果调解和贝叶斯调解方法的发展，这两种方法对 承诺进行调解分析。在研究1中，实际因果调解和贝叶斯调解分析 用于研究设计的开发和评估。这一方法将澄清方法并开发 处理违反可测试和不可测试假设的方法。研究2调查 调解调查的重要计量问题。这项工作将集中在以下方法上 确定中介变量的关键方面，了解调解人和 结果是多余的，并为大数据研究开发方法。研究3继续 用于生态瞬时评估的新型纵向调解方法的开发与评价 数据和其他大量数据收集的研究。这些新方法承诺更准确地 随着时间的推移，个人和个人群体的模型都会发生变化。研究4开发了方法来 揭示调解分析中的子组，包括因果调解方法、多层次模型和新的 基于残差的方法识别调解过程不同的个体 来自其他个人的有效性。对于每项研究，我们将调查与调解有关的独特问题 分析预防数据，包括小N方法和海量数据收集(大数据)， RcErLitEicVaAnl rCoEle(Soeef Imnsetruacstiunrse)：中介机制，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几个美国国立卫生研究院的数据 该项目进一步开发了一种统计调解分析方法，该方法从 资助的预防研究就这些方法的有效性提供了重要的反馈。研究6 研究。调解分析解释了预防和治疗取得成功的方式和原因。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，由 分析改进了预防和治疗，因此它们的影响更大，甚至成本更低。 与研究人员的交流，以及该项目的出版物。