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.

该竞争性延续赠款建议的目的是开发，评估和应用方法和统计程序以调查预防计划如何改变结果变量。这些调解分析评估程序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。如许多研究人员和联邦机构，调解分析确定最有效的计划组件并增加了解导致结果变量变化的基本机制。来自调解分析可以使干预措施更强大，更高效，更短。 P. I. 格兰特获得了为期一年的NIDA小赠款和四项多年赠款，以开发和评估调解预防研究的分析。这项工作导致了许多出版物和创新。提议五年的延续着重于令人兴奋的新调解的进一步发展和完善分析统计发展。四个统计主题代表这项研究的下一步，包括分析和仿真研究以及对病因和预防数据的应用。工作扩展我们的因果调解和贝叶斯调解方法的发展调解分析的承诺。在研究1中，实际因果调解和贝叶斯调解分析为了开发和评估研究设计。这种方法将阐明方法并开发处理违反可检验和不可测试的假设的方法。研究2研究调解调查的重要测量问题。这项工作将集中于方法确定中介变量的关键方面，了解调解人和是否是否结果是多余的，并开发了具有大数据的研究方法。研究3继续生态瞬时评估的新纵向调解方法的开发和评估数据和其他大量数据收集的研究。这些新方法有望更准确个人和个人群体随着时间的流逝而变化。研究4开发方法在调解分析中发现亚组，包括因果中介方法，多级模型和新的基于残差的方法，用于识别中介过程不同的个体其他人的有效性。对于每项研究，我们将研究中介的独特问题预防数据的分析，包括小N和大量数据收集方法（大数据）， rcerliteicvaanl rcoele（soeef imnsetruacstiounrse）：用于中介机制，以及不断增长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几个NIH的数据该项目进一步开发了一种方法，即统计调解分析，该方法从中提取了更多信息资助的预防研究提供了有关方法的有用性的重要反馈。研究6 研究。调解分析解释了预防和治疗方法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，分析改善了预防和治疗，使其影响更大，甚至成本较低。与研究人员和项目的出版物进行沟通。

项目成果

期刊论文数量（40）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Viable Alternative When Propensity Scores Fail: Evaluation of Inverse Propensity Weighting and Sequential G-Estimation in a Two-Wave Mediation Model.

倾向得分失败时的可行替代方案：两波中介模型中逆倾向加权和序贯 G 估计的评估。

DOI：
10.1080/00273171.2019.1614429
发表时间：
2020
期刊：
Multivariate behavioral research
影响因子：
3.8
作者：
Valente,MatthewJ;MacKinnon,DavidP;Mazza,GinaL
通讯作者：
Mazza,GinaL

Item Response Theory Analysis of the Five Facet Mindfulness Questionnaire and its Short Forms.

五方面正念问卷及其简短形式的项目反应理论分析。

DOI：
10.1007/s12671-019-01105-x
发表时间：
2019
期刊：
Mindfulness
影响因子：
3.6
作者：
Pelham3rd,WilliamE;Gonzalez,Oscar;Metcalf,StephenA;Whicker,CadyL;Scherer,EmilyA;Witkiewitz,Katie;Marsch,LisaA;Mackinnon,DavidP
通讯作者：
Mackinnon,DavidP

Evaluating Response Shift in Statistical Mediation Analysis.

DOI：
10.1177/25152459211012271
发表时间：
2021-04
期刊：
ADVANCES IN METHODS AND PRACTICES IN PSYCHOLOGICAL SCIENCE
影响因子：
13.6
作者：
Georgeson, A. R.;Valente, Matthew J.;Gonzalez, Oscar
通讯作者：
Gonzalez, Oscar

Developmental cascade effects of a parenting-focused program for divorced families on competence in emerging adulthood.

DOI：
10.1017/s095457941900169x
发表时间：
2021-03
期刊：
Development and psychopathology
影响因子：
3.3
作者：
Wolchik SA;Tein JY;Winslow E;Minney J;Sandler IN;Masten AS
通讯作者：
Masten AS

Evaluating the Factor Structure of Each Facet of the Five Facet Mindfulness Questionnaire.

评估五方面正念问卷每个方面的因素结构。

DOI：
10.1007/s12671-019-01235-2
发表时间：
2019
期刊：
Mindfulness
影响因子：
3.6
作者：
Pelham3rd,WilliamE;Gonzalez,Oscar;Metcalf,StephenA;Whicker,CadyL;Witkiewitz,Katie;Marsch,LisaA;Mackinnon,DavidP
通讯作者：
Mackinnon,DavidP