Application of Bayesian Methods in Multilevel and Logitudinal Mediation Models

描述(申请人提供)：我提出的研究的目标是开发贝叶斯技术来测试具有多个测量波的集群随机设计中的中介效应。中介分析研究干预措施通过干预变量实现其效果的过程，这些变量是针对变化的。由于广泛使用的最大似然方法依赖于大样本和正态理论来产生有效的结果，因此贝叶斯技术有望在小到中等样本量下表现出优越的性能，特别是在药物滥用预防试验中常见的非正态数据的情况下。此外，贝叶斯方法可以结合以前研究的信息，进一步提高它们的效率。目标1将开发贝叶斯技术来测试集群、随机和纵向模型中的中介效果，这些模型可以结合先前实验的信息并处理非正态数据。由此产生的估计可能会导致比目前的方法更可靠的估计和更大的统计能力。目的2发展马尔可夫链蒙特卡罗(MCMC)方法来估计贝叶斯中介模型。此外，还将编写在公共免费软件包(例如，R、WinBUGS)中实现MCMC方法的计算机代码，使其他研究人员能够访问这项工作。目的3利用模拟现有药物预防分组随机试验和纵向研究的数据结构，对贝叶斯估计器和最大似然估计器的性能进行模拟研究。最重要的是，簇的大小、簇的数量和非正态程度将是不同的。目的4将贝叶斯和现有的频繁信息系统应用于三个现有的毒品预防数据集的多水平方法，以比较每个模型的中介效应的点估计和区间估计的性能。最大似然法和贝叶斯方法的性能作为样本大小的函数的蒙特卡罗比较将使用来自大量现有数据集的重复随机样本进行。该提案旨在改进统计方法，分析药物预防领域多波测量的随机对照试验的数据。提出的方法在参与者人数不多的情况下提供了新的方法，并提高了现有统计方法的有效性和药物预防和健康科学结果的可解释性。我的最终职业目标是成为一名心理学、教育学或健康科学方面的教员。我希望开发和推广在预防药物滥用的基础心理社会研究和随机试验中应用的定量方法。我希望为有助于预防研究人员了解预防干预措施实现其效果的过程的统计和方法基础作出贡献。与公共卫生相关：我对这一奖学金的近期目标有两个。首先，我将进一步加强对贝叶斯统计和中介模型的理解。我将继续在我的委员会的指导下进行监督阅读，特别是我的联席主席大卫·麦金农教授和斯蒂芬·韦斯特教授，他们是中介模型、纵向数据分析和研究设计方面的专家，以及罗伊·利维教授，他是贝叶斯统计方面的专家。我提出了一项全面的审查建议，我的委员会已经批准了一份关于贝叶斯调解办法的广泛论文，这将进一步加强我对拟议项目的准备。我希望在2009年5月，也就是资金周期开始之前，完成我关于贝叶斯统计方法的综合试卷。第二，我将进一步加强我的预防研究培训，包括关于预防研究方法、预防方法和制定预防干预措施的课程。

DESCRIPTION (provided by applicant): The goal of my proposed research is to develop Bayesian techniques to test mediational effects in cluster randomized designs with multiple measurement waves. Mediational analysis studies the processes through which interventions achieve their effects through intervening variables that are targeted for change. Because the widely used maximum likelihood approach relies on large samples and normal theory to produce valid results, Bayesian techniques are expected to show superior performance in small to moderate sample sizes particularly with the non-normal data common in substance abuse prevention trials. In addition, Bayesian methods can incorporate information from previous studies further adding to their efficiency. Aim 1 will develop Bayesian techniques to test mediation effects in cluster randomized and longitudinal models that can incorporate information from previous experiments and handle non-normal data. The resulting estimates can potentially lead to more reliable estimates and greater statistical power than current approaches. Aim 2 develops Markov Chain Monte Carlo (MCMC) methods to estimate Bayesian mediation models. Also, computer code to implement MCMC methods in publicly free software packages (e.g., R, WinBUGS) will be written, making this work accessible to other researchers. Aim 3 conducts a simulation study to compare the performance of the Bayesian and maximum likelihood estimators using data structures that mimic existing drug prevention cluster randomized trials and longitudinal studies. Of most interest, cluster size, number of clusters, and degree of non-normality will be varied. Aim 4 applies both Bayesian and existing frequent is to multilevel methods to three existing data sets on drug prevention to compare the performance of the point and interval estimators of the mediation effects from each model. A Monte Carlo comparison of the performance of maximum likelihood and Bayesian approaches as a function of sample size will be conducted using repeated random samples from a large existing data set. The proposal aims to improve statistical methodology in analyzing data from randomized control trials with multiple waves of measurement in the drug prevention areas. The proposed methodology offers new methods when the number of participants is not large and enhances validity of existing statistical methods and the interpretability of the results in drug prevention and health science. My ultimate career goal is to become a faculty member in psychology, education, or the health sciences. I wish to develop and extend quantitative methods that have application in basic psychosocial research and randomized trials on substance abuse prevention. I hope to contribute to the statistical and methodological foundation that will be useful to prevention researchers in understanding the processes through which preventive interventions achieve their effects. PUBLIC HEALTH RELEVANCE: My immediate goals for this fellowship are twofold. First, I will further strengthen my understanding of Bayesian statistics and mediational models. I will continue to do supervised reading under the direction of my committee, notably my co-chairs professors David MacKinnon and Stephen West who are experts in mediation models, longitudinal data analysis, and research design and Professor Roy Levy who is an expert on Bayesian statistics. I have proposed and my committee has approved a comprehensive examination proposal for an extensive paper on Bayesian approaches to mediation which will further strengthen my preparation for the proposed project. I expect to complete my comprehensive examination paper reviewing Bayesian statistical approaches by May, 2009, prior to the beginning of the funding cycle. Second, I will further enhance my training in prevention research, including classes on prevention research methods approaches to prevention and the development of preventive interventions