Longitudinal Methods for Complex Interactions in Elderly Populations

老年人群复杂相互作用的纵向方法

• 批准号: 7916432
• 负责人: Edward Haksing Ip
• 金额: $ 42.21万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7916432
• 关键词: Affect; Age; Aging; Aging-Related Process; Behavioral; Belgium; Biometry; Body Composition; Cardiovascular system; Chronic; Clinical; Clinical Trials; Collaborations; Comorbidity; Complex; Computer software; Control Groups; Data; Data Analyses; Data Set; Development; Disease; Elderly; Epidemiologic Studies; Epidemiology; Equation; Event; Exercise; Fertilization; Future; Geriatrics; Gills; Health; Health Status; Heart failure; Human; Individual; Intervention; Investigation; Life; Linear Models; Literature; Measurement; Medical; Mental Depression; Methodology; Methods; Modeling; Multivariate Analysis; NIH Program Announcements; Obesity; Outcome; Pattern; Personal Satisfaction; Pharmaceutical Preparations; Policies; Policy Maker; Population; Process; Psychometrics; Recurrence; Research; Research Personnel; Resources; Risk; Risk Factors; Sample Size; Scientist; Signal Transduction; Social Sciences; Solutions; Staging; Stroke; Surrogate Markers; Time; Universities; Variant; age related; base; behavioral/social science; computer program; design; disability; experience; forest; group intervention; interest; intervention effect; lifestyle intervention; longitudinal analysis; markov model; medical schools; member; method development; programs; psychosocial; public health relevance; response; social; stressor; tool; trait; user-friendly

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.

描述（由申请人提供）：该项目旨在开发用于分析从生物医学研究中收集的纵向数据中的复杂相互作用的方法，这些数据包含强大的社会和行为成分。特别令人感兴趣的是收集的数据对老年人口，其中各种形式的相互作用之间和内部的多种危险因素，共病，同时干预，并与时间是司空见惯的。除了这种复杂性之外，这种相互作用还可能影响一种或多种结果。使相互作用研究可操作化的一个起点涉及一个因素的影响（例如，干预）对结果随时间的变化的影响，或所谓的“时间干预”相互作用。对于临床试验，最重要的研究问题通常涉及“时间干预”的相互作用。通过时间相互作用的统计学显著干预将表明干预组和对照组的平均响应模式随时间而不同，因此将指向干预的显著效果。流行病学研究比较两组受试者的风险条件，如抑郁症的心理社会压力，也取决于调查的“风险条件的时间”的相互作用。在老年人群的研究中，普遍存在的相当复杂的相互作用的风险因素的影响和缺乏专门的方法来分析这种相互作用的动机，本建议的重点是进一步发展的方法，超越分析简单的，往往是线性的“干预时间”的相互作用的影响。该提案的具体目标包括进一步开发和扩展两种相对较新的方法--用于分析多种结果和健康状态之间过渡的隐马尔可夫模型，以及用于分析纵向多变量结果数据的广泛生存边缘模型框架。而第二种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个有意义的数据集。这3个数据集分别来自健康、衰老、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）。还将探索集成隐马尔可夫模型和生存边际模型的模型。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方便用户的计算机程序。拟议的跨学科研究计划还旨在促进团队成员之间的科学思想和解决方案的交叉施肥-无论是通过方法开发工作，还是跨应用程序-并充分实现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。公共卫生关系：随着时间的推移，多种临床风险状况、心理社会压力源、共病和同时药物治疗之间的复杂相互作用在老年人群中很常见。该项目利用临床，社会和方法科学家组成的跨学科团队，开发了一系列先进的专业分析工具，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。