Estimating Mediation Effects in Prevention Studies

描述（由申请人提供）：本竞争持续拨款提案的目的是继续开发，评估和应用方法和统计程序，以确定预防项目如何改变结果变量。这些中介分析评估了项目对预防项目目标结构的影响与对结果的影响之间的联系。正如许多研究人员所指出的那样，中介分析确定了最有效的程序组成部分，并增加了对导致改变结果变量的潜在过程的理解。该资助的p.i.获得了为期一年的NIDA小额资助和两笔为期四年的资助，用于开发和评估预防研究中常用的设计和程序的中介分析方法和统计程序。拟议的为期五年的继续研究侧重于研究人员的实际需求，将最新的统计发展应用于中介分析，并为新的预防研究设计开发方法。四个统计主题代表了本研究的下一步，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开发了评估中介模型假设的违反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，以调查和检验中介分析的假设。研究3发展并评估了纵向中介方法，超越了先前资助研究的潜在增长曲线模型。将彻底研究两波和三波或更多波数据的中介分析的概念和统计替代方案。将开发和比较基于自回归、潜差和指数衰减微分方程模型的新的替代性纵向中介模型。研究4评估了我们在先前研究中开发的一般模型，该模型用于调查在参与者亚组不同的情况下存在调节效应或程序效应的中介作用。调节效应在预防研究中很常见。本研究还将纳入一般中介和调节模型的纵向关系，并调查预防科学中常见的效应类型。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，这些数据集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、与研究人员的交流以及项目出版物传播有关中介分析的新信息。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，使其效果更大，甚至成本更低。

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.