Methods of Studying Variability as a Predictor of Health Status

研究变异性作为健康状况预测因子的方法

• 批准号: 8143266
• 负责人: MICHAEL R. ELLIOTT
• 金额: $ 5.65万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8143266
• 关键词: Accounting; Affect; Age; Aging; Breslow Thickness; Characteristics; Chronic; Clinical; Cognitive; Cohort Studies; Data; Data Set; Dementia; Development; Disease; Disease Outcome; Elderly; Epidemiology; Event; Future; Glean; Goals; Growth; Health; Health Status; Impaired cognition; Individual; Joints; Length; Major Depressive Disorder; Measurement; Measures; Menopause; Menstrual cycle; Mental Depression; Mental disorders; Methods; Modeling; Moods; Morbidity - disease rate; Myocardial Infarction; Outcome; Patients; Pattern; Persons; Public Health; Research; Research Personnel; Residual state; Risk; Risk Estimate; Risk Factors; Sampling; Statistical Methods; Structure; Symptoms; Testing; Time; Uterine hemorrhage; Woman; Women' s Health; clinically relevant; design; diaries; interest; minor depressive disorder; negative mood; novel; public health relevance; reproductive hormone; statistics; time use; trend

DESCRIPTION (provided by applicant): The purpose of the proposed research is to develop methods to better understand how variability of health measures may be predictive of future health outcomes of interest. Many statistical methods have been developed that treat within-subject correlation that accompanies the clustering of subjects in longitudinal data settings as a nuisance parameter, with the focus of analytic interest being on mean outcome or profiles over time. However, there is evidence that, at least in certain settings, the underlying variability in subject measures may also be important in predicting future health outcomes of interest. Hence we plan to develop methods that will better structure variability, decomposing it into short-term and long-term variance measures, and combining variance structures with mean structures such as mean longitudinal profile to more fully describe the information available in longitudinal datasets. In particular, we propose methods to jointly model mean profile and variance in continuous longitudinal data, including methods that treat variance as heteroscedastic within individuals as well as between individuals. We also propose methods to jointly model short-term and long- term variance in continuous longitudinal data. We will apply these methods to the analysis of within-woman trends and variability in reproductive hormones and time between menstrual cycles to predict the progression of health outcomes through the transition to menopause, and to the analysis of within-person trends and variability in cognitive testing to predict cognitive decline and progression of dementia in older adults. PUBLIC HEALTH RELEVANCE: Public Health Relevance As more clinical and public health studies follow individuals through time, it becomes possible to consider whether variability in measures over time is an important predictor of disease. Most methods for studying such data focus on differences in averages and average trends across individuals. Our proposed study will consider whether adding information about variability over time will assist in predicting and ultimately better understanding the cause of various diseases, particularly chronic conditions associated with aging.

描述（由申请人提供）：拟议研究的目的是开发方法，以更好地了解健康指标的变异性如何预测未来的健康结果。已经开发了许多统计方法，这些方法将伴随纵向数据设置中的受试者聚类的受试者内相关性视为讨厌的参数，分析兴趣的焦点是随时间推移的平均结果或曲线。然而，有证据表明，至少在某些情况下，受试者测量的潜在变异性在预测未来的健康结果方面也可能很重要。因此，我们计划开发更好地结构变异性的方法，将其分解为短期和长期方差度量，并将方差结构与平均结构（如平均纵向剖面）相结合，以更全面地描述纵向数据集中可用的信息。特别是，我们提出的方法来共同建模的平均配置文件和方差在连续的纵向数据，包括方法，处理方差为异方差的个人以及个人之间。我们还提出了在连续纵向数据中联合建模短期和长期方差的方法。我们将应用这些方法来分析女性内生殖激素和月经周期之间的时间的趋势和变异性，以预测通过过渡到更年期的健康结果的进展，并分析认知测试中的人内趋势和变异性，以预测老年人认知能力下降和痴呆症的进展。 公共卫生相关性：公共卫生相关性随着越来越多的临床和公共卫生研究随着时间的推移跟踪个体，有可能考虑随着时间的推移测量的变化是否是疾病的重要预测因素。研究这类数据的大多数方法都侧重于个体之间的平均值和平均趋势的差异。我们提出的研究将考虑增加有关随时间变化的信息是否有助于预测并最终更好地了解各种疾病的原因，特别是与衰老相关的慢性疾病。