Estimating Mediation Effects in Prevention Studies

估计预防研究中的中介效应

• 批准号: 7872772
• 负责人: David P MacKinnon
• 金额: $ 35.45万
• 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 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7872772
• 关键词: Address; Applications Grants; Arts; Bayesian Method; Communication; Data; Data Analyses; Data Set; Development; Effect Modifiers (Epidemiology); Equation; Feedback; Funding; Goals; Grant; Growth; Intervention; Lead; Link; Measurement; Measures; Mediating; Mediation; Mediator of activation protein; Methodology; Methods; Modeling; National Institute of Drug Abuse; Outcome; Outcomes Research; Participant; Prevention; Prevention Research; Prevention program; Procedures; Process; Publications; Research; Research Design; Research Personnel; Residual state; Sample Size; Science; Statistical Methods; Study models; Subgroup; Testing; Time; Translating; United States National Institutes of Health; Work; base; cost; design; improved; interest; multilevel analysis; novel strategies; programs; public health relevance; simulation; web site

DESCRIPTION (provided by applicant): The purpose of this competing continuation grant proposal is to continue developing, evaluating and applying 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, mediation analyses identify the most effective program components and increase understanding of the underlying processes leading to changing outcome variables. The P. I. of this grant received a one-year NIDA small grant and two four-year grants to develop and evaluate mediation analysis methods and statistical procedures for the designs and procedures commonly used in prevention research. The proposed five-year continuation study focuses on practical needs of researchers, applying recent statistical developments to mediation analysis, and developing methods for new prevention research designs. Four statistical topics represent the next steps in this research and will include analytical and simulation research as well as applications to actual etiological and prevention data. In Study 1, practical issues in significance testing are investigated including the best methods for studies with limited sample size and general methods to assess required sample size for any mediation design. Study 2 develops methods to assess how violations of assumptions of the mediation model lead to incorrect conclusions. Study 2 extends new developments in causal inference to mediation analysis to investigate and test assumptions of mediation analysis. Study 3 develops and evaluates longitudinal mediation methods beyond the latent growth curve modeling investigated in prior funding. Conceptual and statistical alternatives for mediation analysis of data from two waves and three or more waves will be thoroughly investigated. New alternative longitudinal mediation models based on autoregressive, latent difference, and exponential decay differential equation models will be developed and compared. Study 4 evaluates a general model developed in our prior research to investigate mediation when there are also moderator effects or program effects that differ by subgroups of participants. Moderator effects are common in prevention research. This study will also incorporate longitudinal relations in the general mediation and moderation model as well as investigate the types of effects common in prevention science. Study 5 applies the statistical methods to data from several NIH funded prevention data sets providing important feedback about the usefulness of the models. Study 6 disseminates new information about mediation analysis through our website, by communication with researchers, and publications from the project. PUBLIC HEALTH RELEVANCE: The project investigates a statistical method, mediation analysis that extracts more information from research. Mediation analysis provides information on how and why prevention and treatments are successful. Mediation analysis can improve prevention and treatment so that they have greater effects and even cost less.

描述(由申请人提供)：这项竞争性延续拨款提案的目的是继续开发、评估和应用方法学和统计程序，以确定预防方案如何改变结果变量。这些中介分析评估预防计划所针对的结构上的计划效果和对结果的影响之间的联系。正如许多研究人员指出的那样，中介分析确定了最有效的程序组件，并增加了对导致结果变量变化的潜在过程的理解。这笔赠款的私人投资机构获得了一笔为期一年的NIDA小额赠款和两笔为期四年的赠款，用于为预防研究中常用的设计和程序制定和评估调解分析方法和统计程序。拟议的五年延续研究侧重于研究人员的实际需要，将最新的统计发展应用于调解分析，并为新的预防研究设计开发方法。四个统计主题代表了这项研究的下一步，将包括分析和模拟研究以及对实际病因学和预防数据的应用。在研究1中，研究了显著性检验中的实际问题，包括有限样本量研究的最佳方法和评估任何中介设计所需样本量的一般方法。研究2开发了评估违反调解模型的假设如何导致错误结论的方法。研究2将因果推理的新发展扩展到中介分析，以考察和检验中介分析的假设。研究3开发和评估了超越先前资金调查的潜在增长曲线模型的纵向调节方法。将深入研究两波和三波或更多波数据的调解分析的概念和统计备选办法。基于自回归、潜差和指数衰减微分方程模型的新的可选纵向中介模型将被开发和比较。研究4评估了我们先前研究中开发的一个通用模型，该模型研究了当参与者的不同亚组也存在不同的主持人效应或程序效应时的调解。缓和效应在预防研究中很常见。这项研究还将把纵向关系纳入一般调解和调节模型，并调查预防科学中常见的影响类型。研究5将统计方法应用于来自NIH资助的几个预防数据集的数据，提供了关于模型有用性的重要反馈。研究6通过我们的网站、与研究人员的交流和该项目的出版物传播关于调解分析的新信息。公共卫生相关性：该项目调查了一种统计方法，即从研究中提取更多信息的中介分析。调解分析提供了关于预防和治疗如何以及为什么成功的信息。调解分析可以改善预防和治疗，从而产生更大的效果，甚至更低的成本。