Estimating Mediation Effects in Prevention Studies

估计预防研究中的中介效应

• 批准号: 9086292
• 负责人: David P MacKinnon
• 金额: $ 34.44万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-07-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9086292
• 关键词: Address; Advocate; Affect; Applications Grants; Bayesian Method; Bayesian Modeling; Behavioral Mechanisms; Communications Media; Consultations; Data; Data Analyses; Data Collection; Data Set; Development; Differential Equation; Ecological momentary assessment; Educational workshop; Equation; Evaluation; Feedback; Funding; Future; Goals; Grant; Growth; Health; Individual; Individual Differences; Intervention; Investigation; Link; Measurement; Measures; Mediating; Mediation; Mediator of activation protein; Methodology; Methods; Modeling; National Institute of Drug Abuse; Outcome; Prevention; Prevention Research; Prevention program; Probability; Procedures; Process; Publications; Recommendation; Research; Research Design; Research Methodology; Research Personnel; Science; Statistical Data Interpretation; Statistical Methods; Subgroup; Testing; Time; Translating; United States National Institutes of Health; Weight; Work; base; cost; design; improved; innovation; interest; multilevel analysis; practical application; programs; simulation; theories; tool; web site

 DESCRIPTION (provided by applicant): The purpose of this competing continuation grant proposal is to develop, evaluate and apply methodological and statistical procedures to determine 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 programs more powerful, more efficient, and shorter. The P. I. of this grant received a one-year NIDA small grant and three 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 practical use 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. In Study 1, practical issues in the application of recently developed methods, causal mediation and Bayesian mediation analyses, are investigated. Causal mediation and Bayesian methods have the capability to greatly improve the accuracy of mediation analyses. Study 2 investigates solutions to two common problems in prevention research, measurement error and confounder bias, that can make mediation analysis inaccurate. Analyses to correct mediation analysis as well as methods to assess how these problems could affect results will be developed and evaluated. Study 3 develops and evaluates newly developed longitudinal mediation methods based on causal effects and also new methods for data with many repeated measurements. These new methods promise to more accurately model change over time. Study 4 evaluates methods to uncover subgroups in mediation analysis including causal mediation methods and multilevel models for groups and individuals. For each study, we will investigate unique issues with mediation analysis of prevention data including information obtained prior to a study that improve mediation analysis, the timing of change in longitudinal mediation models, and the types and importance of subgroup effects. Study 5 applies new statistical methods to data from several NIH funded prevention data sets providing important feedback about the usefulness of the methods. Study 6 disseminates new information about mediation analysis through our website and other media, by communication with researchers, and publications from the project.

 描述（由申请人提供）：本竞争性继续拨款提案的目的是开发、评估和应用方法和统计程序，以确定预防计划如何改变结果变量。这些中介分析评估了计划对预防计划所针对的结构的影响与对结果的影响之间的联系。正如许多研究人员和联邦机构所指出的，调解分析确定了最有效的计划组成部分，并增加了对导致结果变量变化的潜在机制的理解。来自中介分析的信息可以使程序更强大、更高效、更短。此项资助的 P.I. 获得了为期一年的 NIDA 小额资助和三项多年期资助，用于开发和评估预防研究中的中介分析。这项工作催生了许多出版物和创新。拟议的五年延续重点是令人兴奋的新中介分析统计发展的实际应用。四个统计主题代表了这项研究的后续步骤，包括分析和模拟研究以及病因学和预防数据的应用。在研究 1 中，研究了最近开发的方法、因果中介和贝叶斯中介分析的应用中的实际问题。因果中介和贝叶斯方法能够大大提高中介分析的准确性。研究 2 研究了预防研究中两个常见问题的解决方案：测量误差和混杂偏差，这些问题可能导致中介分析不准确。将开发和评估纠正中介分析的分析以及评估这些问题如何影响结果的方法。研究 3 开发并评估了新开发的基于因果效应的纵向中介方法以及针对多次重复测量的数据的新方法。这些新方法有望更准确地模拟随时间变化的情况。研究 4 评估了在中介分析中揭示亚组的方法，包括因果中介方法以及群体和个人的多层次模型。对于每项研究，我们将调查预防数据中介分析的独特问题，包括在改进中介分析的研究之前获得的信息、纵向中介模型的变化时间以及亚组效应的类型和重要性。研究 5 将新的统计方法应用于 NIH 资助的几个预防数据集中的数据，提供有关该方法的有用性的重要反馈。研究 6 通过我们的网站和其他媒体、与研究人员的沟通以及项目的出版物传播有关中介分析的新信息。