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）：这项竞争性继续资助提案的目的是开发、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项目对预防项目所针对的结构的影响和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方案组成部分，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、更简短。私家侦探。在这笔赠款中，有20%的人获得了一笔为期一年的NIDA小额赠款和三笔多年赠款，用于开发和评估预防研究中的调解分析。这项工作导致了许多出版物和创新。拟议的五年延续期侧重于实际使用令人兴奋的新调解分析统计发展。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。在研究1中，最近开发的方法，因果调解和贝叶斯调解分析的应用中的实际问题进行了调查。因果调解和贝叶斯方法有能力大大提高调解分析的准确性。研究2探讨了预防研究中两个常见问题的解决方案，测量误差和混淆偏差，这可能会使中介分析不准确。将制定和评价纠正调解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开发和评估新开发的纵向中介方法的因果关系的基础上，也有新的方法，许多重复测量的数据。这些新方法有望更准确地模拟随时间的变化。研究4评估的方法，发现亚组的中介分析，包括因果中介方法和多层次模型的群体和个人。对于每项研究，我们将调查独特的问题与预防数据的中介分析，包括信息之前获得的研究，提高中介分析，纵向中介模型的变化的时间，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来自NIH资助的几个预防数据集的数据，这些数据集提供了有关方法有用性的重要反馈。研究6通过我们的网站和其他媒体，通过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。