ESTIMATING MEDIATION EFFECTS IN PREVENTION STUDIES

描述：(申请人摘要) 这项建议的目的是发展方法学和统计学 确定预防计划如何改变结果变量的程序。 这些中介分析评估了计划对 预防计划所针对的结构及其对结果的影响。AS 许多研究人员注意到，调解分析确定了最有效的 规划组件并增加对基础流程的理解 导致--吸毒。 这些新程序的必要性在以下几个方面的分析中变得明显 NIDA资助了毒品预防项目。这笔赠款的私家侦探收到了一份 NIDA一年小额赠款，用于研究调解效果的评估。在……里面 这项提案，我们要求四年的资金来开发方法和 较复杂类型的设计和设计的统计程序 预防研究中常用的程序。在七年中的第一天 建议的研究，我们检查类型1的错误和检验的统计能力 调解。在研究2中，中介效应在多个中介和 对纵向模型进行了检验。在研究3中，我们开发和评估了 非等价对照组中介效应的估计方法 设计。在研究4中，我们开发和评估了估计中介的方法 Logistic回归和比例风险模型中的作用。在学习中 5、我们发展和评估了调解效果的评估方法。 各种设计，包括分层回归模型和模型 既有主持人又有调解人的作用。在研究6中，我们检查了 缺失数据、测量误差、非正态数据和异常值对 对中介效应的估计。在研究7中，我们将新方法应用于 NIDA资助了几个毒品预防项目。作为研究7的一部分，我们将 通过建立协商机制增加调解方式的使用 服务和一个交互式计算机程序，教研究人员如何 进行调解分析。

DESCRIPTION: (Applicant's Abstract) The purpose of this proposal is to develop 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 outcomes. As noted by many researchers, mediation analyses identify the most effective program components and increase understanding of the underlying processes leading to-drug use. The need for these new procedures became apparent in the analyses of several NIDA funded drug prevention programs. The P.I. of this grant received a one-year NIDA small grant to study the assessment of mediation effects. In this proposal, we request four years of funding to develop methods and statistical procedures for the more complicated types of designs and procedures commonly used in prevention research. In the first of seven proposed studies, we examine type 1 error and statistical power of tests of mediation. In Study 2, mediated effects in multiple mediator and longitudinal models are examined. In Study 3, we develop and evaluate methods to estimate mediated effects in the nonequivalent control group design. In Study 4, we develop and evaluate methods to estimate mediated effects in logistic regression and the proportional hazards model. In Study 5, we develop and evaluate methods to estimate mediation effects in a variety of designs, including the hierarchical regression model and models with both moderator and mediator effects. In Study 6, we examine the effects of missing data, measurement error, non-normal data, and outliers on the estimation of mediated effects. In Study 7, we apply the new methods in several NIDA funded drug prevention projects. As part of Study 7, we will increase the use of mediation methods by the establishment of a consultation service and an interactive computer program to teach researchers how to conduct mediation analysis.