STATISTICAL ANALYSIS OF BODY COMPOSITION RISK FACTORS

身体成分风险因素统计分析

• 批准号: 2403214
• 负责人: SHUMEI S SUN
• 金额: $ 15.11万
• 依托单位: WRIGHT STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-08-01 至 1998-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2403214
• 关键词: body composition; body weight; cardiovascular disorder epidemiology; child physical development; computer program /software; disease /disorder proneness /risk; human data; hypertension; longitudinal human study; mathematical model; model design /development; statistics /biometry

The overall goal is to bridge the gap between recent statistical advances and current practice and to improve statistical methods so as to increase the applicability of their use in studies of human body composition and risk factors for cardiovascular and related diseases. Existing data from a longitudinal study of body composition and cardiovascular disease risk factors will be used to provide numerical examples and to demonstrate the application of the developed statistical methods. Specifically the aims are: (i) A random effects method will be extended by incorporating a time series process for the within-subject errors to ensure an adequate description of patterns of change over time and the accurate prediction of future values and future risk. The risk of elevated levels of future values conditioned on earlier values will be investigated. (ii) A kernel estimator will be investigated further to include: (i) the implementation of cross-validation to select the best combination of a smoothing parameter and a weight function, (ii) derivation of confidence intervals for the estimator and the predicted values, (iii) development of the velocity and acceleration of the fitted curve, and (iv) characterization of the landmark points such as maximum velocity, age at maximum, and predicted value at maximum velocity. These proposed statistical developments are motivated by the problems that occur in modeling irregular body composition and risk factors data from longitudinal designs. Patterns of change in body composition and risk factors are irregular among individuals which leads to the misspecification of the model or models describing these patterns. Consequently, the estimated coefficients and the predicted values are biased. The extension of random effects method and the further development of the kernel regression will improve the prediction of future values from earlier values. The ability to accurately predict future values from earlier values is important in studies of cardiovascular and related diseases where early intervention in childhood may prevent the occurrence of aberrant levels in adulthood. Although the investigation will be performed on body composition data and risk factors for cardiovascular and related diseases, these statistical methods will be generalizable to any serial data in any gender or minority population.

总体目标是弥合最近的统计进展之间的差距 和目前的做法，并改进统计方法，以增加 它们在人体组成和研究中的适用性 心血管及相关疾病的风险因素。现有数据来自 身体成分与心血管疾病风险的纵向研究 我们将使用系数来提供数值例子，并演示 应用已开发的统计方法。具体而言，目标是 包括： (I)随机效果方法将扩展，纳入一个时间 针对受试者内部错误的系列过程，以确保充分的 描述随时间变化的模式并准确预测 未来价值和未来风险。未来水平升高的风险 将调查以较早值为条件的值。 (2)将进一步调查核估计器，以包括： (一)实施交叉验证，选择最佳组合 平滑参数和权函数的关系，(Ii)导出 估计值和预测值的可信区间，(Iii) 拟合曲线的速度和加速度的发展，以及(Iv) 标志点的特征，如最大速度、年龄 最大值和最大速度下的预测值。 这些拟议的统计发展是由以下问题推动的 在对不规则的身体成分和危险因素数据进行建模时发生 纵向设计。身体成分和风险的变化模式 个体之间的因素是不规则的，这导致了 错误指定描述这些模式的一个或多个模型。 因此，估计系数和预测值为 有偏见。随机效应方法的推广及进一步发展 将改进对未来值的预测，从 较早的值。准确预测未来价值的能力 早期价值在心血管及相关研究中很重要 儿童早期干预可预防其发生的疾病 成年期的异常水平。 虽然调查将基于身体成分数据和 心血管及相关疾病的风险因素，这些统计数据 方法将适用于任何性别或少数民族的任何系列数据 人口。