Methods of Studying Variability as a Predictor of Health Status

描述（由申请人提供）：拟议研究的目的是开发方法，以更好地了解健康测量的可变性如何预测未来感兴趣的健康结果。已经开发了许多统计方法，这些方法将纵向数据设置中伴随着受试者聚类的受试者内部相关性视为一个讨厌的参数，分析兴趣的重点是随着时间的推移的平均结果或概况。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也可能对预测未来的健康结果很重要。因此，我们计划开发更好地结构化变异的方法，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（如平均纵剖面）相结合，以更全面地描述纵向数据集中的可用信息。特别是，我们提出了在连续纵向数据中联合建模均值和方差的方法，包括将方差视为个体内部和个体之间的异方差的方法。我们还提出了在连续的纵向数据中联合建模短期和长期方差的方法。我们将把这些方法应用于分析女性体内生殖激素的趋势和可变性以及月经周期之间的时间，以预测过渡到更年期后健康结果的进展，并应用于分析认知测试中的个人趋势和可变性，以预测老年人认知能力下降和痴呆的进展。

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.