Analyzing Complex Longitudinal Data in Behavior Sciences

描述（由申请人提供）：吸烟和肥胖是癌症、心脏病和ii型糖尿病等不良健康结果的两种主要前兆，因此是行为改变的临床和公共卫生研究的焦点。许多行为医学的纵向临床试验旨在影响相关行为的积极和健康的改变，例如通过促进戒烟，锻炼和合理饮食。这些试验数据的分析和报告可能会因数据缺失、受试者对治疗的不依从性和随访缺失而变得复杂。此外，研究人员经常对结果感兴趣，而不是像“戒烟”（是/否）或“平均体重变化”这样的简单总结。由于试验是纵向的，因此数据可以深入了解行为改变的过程，并可用于分类不同的行为改变模式。此外，纵向数据的可用性提供了对最终结果进行预测的机会，这些预测可以动态地用于治疗某些行为。该项目将开发创新的方法来分析纵向数据，特别是为行为医学试验。该项目的前两个组成部分将开发潜在类别和潜在变量模型，用于对戒烟和体重变化中的行为变化模式进行分类。特别是对于戒烟试验，通常的潜在类和随机效应建模方法是不充分的，因为随机效应正常的标准假设很少是充分的，而通常的互换性假设在实践中通常不满足。本研究的第三个目标是解决信息缺失数据的问题，为模式混合建模和相关敏感性分析提供一个统一和连贯的框架。这部分建议的工作将把纵向数据的PM建模的几个最近的建议联系在一起。最后，我们的第四个目标将通过开发工具变量（IV）和g计算方法来解决不合规的重要问题。我们所有的工作都将在贝叶斯框架中进行，因此提案的主要部分将涉及开发和实施适当的算法来模拟相当复杂模型的后验分布。所有的方法都将基于并应用于最近完成或正在进行的行为医学试验的数据，特别是在戒烟和体重改变方面，这些试验由该领域公认的领导者进行。

DESCRIPTION (provided by applicant): Smoking and obesity are two of the leading precursors to adverse health outcomes such as cancer, heart disease, and type-II diabetes, and therefore are a focal point of clinical and public health research on behavior change. Many longitudinal clinical trials in behavioral medicine are designed to effect positive and healthy changes in related behaviors, for example by promoting smoking cessation, exercise, and sensible diets. The analysis and reporting of data from these trials can be complicated by missing data, subject noncompliance with therapy, and loss to follow up. Furthermore, researchers are frequently interested in outcomes beyond the usual simple summaries such as 'quit smoking' (yes/no) or 'average weight change.' Because trials are longitudinal, the data can give insight into the process of behavior change, and can be used to classify different patterns of behavior change. Moreover, the availability of longitudinal data provides the opportunity to develop predictions of eventual outcomes that can be used dynamically in the treatment of certain behaviors. This project will develop innovative approaches to the analysis of longitudinal data specifically for behavioral medicine trials. The first two components of the project will develop latent class and latent variable models for classifying patterns of behavior change in smoking cessation and weight change. For smoking cessation trials in particular, the usual approaches to latent class and random effects modeling are inadequate because the standard assumption that random effects are normal rarely is adequate, and the usual exchangeability assumptions are not typically met in practice. The third aim of the study addresses the issue of informative missing data, presenting a unifying and coherent framework for pattern-mixture modeling and associated sensitivity analyses. This part of the proposed work will tie together several recent proposals for PM modeling of longitudinal data. Finally, our fourth aim will address the important issue of noncompliance by developing both instrumental variable (IV) and G-computation approaches to causal inference. All of our work will be carried out in a Bayesian framework, so a major part of the proposal will be concerned with developing and implementing appropriate algorithms for simulating posterior distributions of fairly complex models. All of the methods will be developed on and applied to data from recently completed or ongoing trials in behavioral medicine, particularly in smoking cessation and weight change, run by recognized leaders in the field.