Longitudinal Methods for Complex Interactions in Elderly Populations

描述(由申请人提供)：该项目旨在开发分析从生物医学研究中收集的包含强烈社会和行为成分的纵向数据中的复杂交互作用的方法。特别令人感兴趣的是收集的关于老年人口的数据，在这些数据中，各种形式的相互作用--在多种风险因素之间和内部、共病、同时干预和与时间--是常见的。增加这种复杂性的是，这种相互作用有可能影响单一或多个结果。交互作用研究的可操作性的一个起点涉及一个因素(例如，干预)对结果随时间变化的影响，或所谓的“按时间干预”交互作用。对于临床试验，最重要的研究问题往往与“按时间干预”的交互作用有关。按时间交互作用进行的统计显著干预将表明，随着时间的推移，干预组和对照组的平均反应模式是不同的，因此将指向干预的显著效果。流行病学研究通过比较两组受试者的风险状况，如抑郁症的心理社会应激源，也取决于对“按时间的风险状况”交互作用的调查。鉴于老年人口研究中普遍存在相当复杂的风险因素交互作用，且缺乏分析这种交互作用的专门方法，本建议侧重于进一步发展超越简单且往往是线性的“按时间进行干预”交互作用的分析方法。该提案的具体目标包括进一步发展和推广两种相对较新的方法--隐马尔可夫模型，用于分析多个结果和健康状态之间的转换，以及一个广泛的生存边际模型框架，用于分析纵向多变量结果数据。第一个子项目特别与年龄相关残疾的建模有关，而第二个子项目适用于分析多变量结果的混合模式，包括复发-复发事件数据。相应地，将使用这些方法分析3个重要的数据集。这3个数据集分别来自健康、老龄化、身体成分(健康ABC)、心血管健康研究(CHS)和老年人生活方式干预和独立性(LIFE)。探讨了隐马尔可夫模型与生存边际模型相结合的模型。该项目的另一个具体目标是在一个共同平台上传播研究成果和用户友好的计算机程序。拟议的跨学科研究计划还旨在促进团队成员之间通过方法开发努力和跨应用程序相互交流科学思想和解决方案，并充分认识到在生物医学和社会科学中收集的纵向数据的质量和科学力量。公共卫生相关性：在老年人群中，随着时间的推移，多种临床风险状况、心理社会应激源、并存疾病和同时进行的医疗之间的复杂相互作用是很常见的。该项目利用临床、社会和方法论科学家组成的跨学科团队，开发了一系列先进的专门分析工具，可以帮助临床医生更好地了解相互交织的因素如何促进人类健康和福祉的机制，特别是老年人的健康和福祉。

DESCRIPTION (provided by applicant): This project aims to develop methods for analyzing complex interactions in longitudinal data collected from biomedical studies that contain a strong social and behavioral component. Of special interest are data collected on elderly populations, within which interaction of various forms-between and within multiple risk factors, co-morbidities, concurrent interventions, and with time-are commonplace. Added to this complexity is the potential of this interaction to affect a single or multiple outcomes. A starting point to operationalize the study of interaction concerns the effect of a factor (e.g., intervention) on the variation of an outcome over time, or the so-called "intervention by time" interaction. For clinical trials, the most important research question often concerns an "intervention by time" interaction. A statistically significant intervention by time interaction would signal that the average response patterns of the intervention group and the control group are different over time, and thus would point to a significant effect of the intervention. Epidemiological studies that compare 2 groups of subjects by a risk condition such as a psychosocial stressor of depression, also hinge on the investigation of the "risk condition by time" interaction. Motivated by the ubiquity of rather complex interaction effects of risk factors in the study of elderly populations and the lack of specialized methods for analyzing such interactions, this proposal focus on the further development of methods that go beyond analyzing the simple and often linear "intervention by time" interaction effects. The specific aims of the proposal include the further development and extension of a of 2 relatively new methods--hidden Markov models for analyzing multiple outcomes and transition between health states, and a broad survival marginal models framework for analyzing longitudinal multivariate outcome data The first subproject is especially relevant to the modeling of age-related disablement, while the second is suitable for analyzing mixed modes of multivariate outcomes, including recurrent-recurrent events data. Correspondingly 3 significant data sets will be analyzed using these methods. These 3 data sets are collected, respectively, from the Health, Aging, Body Composition (Health ABC), the Cardiovascular Health Study (CHS), and Lifestyle Intervention and Independence for Elders (LIFE). A model integrating the hidden Markov model and survival marginal model will also be explored. Another specific aim of the project is to disseminate research results and user-friendly computer programs on a common platform. The proposed interdisciplinary program of study is also designed to promote cross-fertilization of scientific ideas and solutions among team members-both through method development efforts, and across applications--and to fully realize the quality and scientific power of longitudinal data collected in the biomedical and social sciences. PUBLIC HEALTH RELEVANCE: Complex interactions between multiple clinical risk conditions, psychosocial stressors, co-morbidities, and concurrent medical treatments over time are commonplace in elderly populations. Using an interdisciplinary team of clinical, social and methodology scientists, this project develops a selection of advanced specialized analytic tools that could help clinicians better understand the mechanisms of how intertwining factors contribute to human health and well-being, especially that of the elderly.