Estimating Mediation Effects in Prevention Studies

估计预防研究中的中介效应

基本信息

批准号：
10651627
负责人：
David P MacKinnon
金额：
$ 33.74万
依托单位：
ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
依托单位国家：
美国
项目类别：
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/10651627
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 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 Personnel Residual state Statistical Data Interpretation Statistical Methods Subgroup Testing Time Translating United States National Institutes of Health Work computer program cost data integration 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

项目摘要

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.

此竞争性延续拨款提案的目的是开发、评估和应用调查预防计划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计划对目标结构的影响之间的联系预防计划及其对结果的影响。正如许多研究人员和联邦机构所指出的机构、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根本机制。信息来自中介分析可以使干预措施更有力、更高效、更短。这个的 P.I. 获得了为期一年的 NIDA 小额赠款和四项多年期赠款，用于开发和评估调解预防研究中的分析。这项工作催生了许多出版物和创新。拟议的五年的延续专注于令人兴奋的新调解的进一步发展和完善分析统计发展。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病因学和预防数据的应用。工作扩展了我们对因果调解和贝叶斯调解方法的发展，这些方法具有重要意义中介分析的承诺。在研究 1 中，实际因果中介和贝叶斯中介分析用于开发和评估研究设计。这种方法将阐明方法并发展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衡量问题。这项工作将重点关注方法确定中介变量的关键方面，以及理解中介变量和中介变量是否存在的方法结果是多余的，并开发大数据研究方法。研究 3 继续生态瞬时评估新纵向中介方法的开发和评估数据和其他具有大量数据收集的研究。这些新方法有望更准确地个人和个人群体的模型随着时间的推移而变化。研究 4 开发了方法揭示中介分析中的亚组，包括因果中介方法、多层次模型和新的中介方法基于残差的方法用于识别调解过程不同的个体其他人的有效性。对于每项研究，我们都会调查调解的独特问题预防数据分析包括小N的方法以及海量数据收集（大数据）， RcErLitEicVaANl rCoEle(Soeef imnsetruacstiounrse)：调解机制的研究，以及越来越多的文献的应用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多个 NIH 的数据该项目进一步开发了一种方法，即统计中介分析，可以从资助的预防研究提供有关这些方法的有用性的重要反馈。研究 6 研究。中介分析解释了预防和治疗如何以及为何成功。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以改善预防和治疗，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。