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，以更好地了解健康指标的可变性如何预测感兴趣的未来健康结果。已经开发了许多统计方法，将在纵向数据环境中伴随着对象聚类的对象内相关性视为干扰参数，分析兴趣的焦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在预测感兴趣的未来健康结果方面可能也很重要。因此，我们计划开发更好的结构可变性的方法，将其分解为短期和长期的方差度量，并将方差结构与均值结构(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，我们提出了对连续纵向数据中的平均轮廓和方差进行联合建模的方法，包括将个体内和个体之间的方差视为异方差的方法。我们还提出了对连续纵向数据的短期方差和长期方差进行联合建模的方法。我们将应用这些方法来分析女性内部生殖激素的趋势和变异性以及月经周期之间的时间，以预测更年期过渡期间健康结果的进展，并应用这些方法分析认知测试中的人内趋势和变异性，以预测老年人的认知衰退和痴呆症的进展。 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对个人进行跟踪，人们有可能考虑到测量结果随时间的变化是否是疾病的重要预测因素。大多数研究这类数据的方法都集中在个人平均水平和平均趋势上的差异。我们建议的研究将考虑增加关于随时间变化的信息是否有助于预测并最终更好地理解各种疾病的原因，特别是与衰老相关的慢性疾病。

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.