STATISTICAL ANALYSIS OF BODY COMPOSITION RISK FACTORS

描述：（改编自申请人的描述）：本申请 研究是对先前工作的继续和扩展 申请人在当前的项目中。 总体目标是应用 最新的、先进的、统计学的方法来研究人体 心血管疾病和相关疾病的组成和风险因素。 心血管疾病风险纵向研究的现有数据 包括身体成分测量在内的因素将用于测试 先进统计方法的适用性。 两个具体 本申请的目的是：1）确定统计模型或 调整可用于说明测量的影响 纵向研究中的误差，关于回归估计， 假设检验和模型预测;测量误差可以 简化因变量和自变量之间的关系 导致回归估计值的偏差， 假设检验，并减少预测的普遍性， 未来风险; 2）评估同时使用几种风险 多变量、纵向模型中的因子，以改善预测 未来的风险。 这些建议的统计改进的动机是研究 心血管疾病的长期系列危险因素， 这些研究中引入的测量误差所产生的问题， 特别是那些与身体成分有关的错误 测量. 独立测量误差的影响 将对纵向模型的变量进行检查和调整。 从早期值准确预测未来值的能力， 例如儿童期，在许多纵向研究中很重要， 可能有助于识别或预测与增加的 风险 然而，一个变量的变化模式可能是 受其他变量的影响，因此， 风险因素之间的多变量关系有助于 推导和澄清多个变量之间的关系， 并允许改进的预测模型。 研究者 应用是同时使用几种危险因素（参见， 一个单一的风险因素）将改善个人的识别 未来的风险。 在这项工作中提出的模型开发将使用现有的数据， 身体成分和心血管及相关疾病的危险因素 Fels纵向研究中的疾病。 申请书指出， 要开发的模型不仅限于这些数据，而且可以 用于其他纵向数据的分析和解释 集.

DESCRIPTION: (Adapted from Applicant's Description): This proposed research is a continuation and expansion of preceding work performed by this applicant in a current project. The overall goals are to apply recent, advanced, statistical methods to studies of human body composition and risk factors for cardiovascular and related diseases. Existing data from a longitudinal study of cardiovascular disease risk factors, including body composition measurements, will be used to test the applicability of the advanced statistical methods. Two Specific Aims of this application are: 1) to determine if statistical models or adjustments can be used to account for the effects of measurement errors in longitudinal studies, regarding the regression estimates, hypothesis testing, and model predictions; measurement errors can reduce relationships between dependent and independent variables resulting in biases for regression estimates, reduced validity of hypothesis testing, and reduced generalizability of the prediction of future risk; and 2) to evaluate the simultaneous use of several risk factors in multivariate, longitudinal models to improve the prediction of future risk. These proposed statistical refinements are motivated by the study of long-term serial risk factors for cardiovascular diseases and the problems generated by measurement errors introduced into these studies, in particular those errors associated with body composition measurements. The impact of measurement errors in independent variables on longitudinal models will be examined and adjusted. The ability to accurately predict future values from earlier values, such as in childhood, is important in many longitudinal studies, and may help to identify or predict those values associated with increased risk. Nevertheless, patterns of change in one variable can be influenced by other variables, hence statistical modeling of the multivariate relationships among risk factors should aid in the derivation and clarification of relationships among multiple variables, and allow for improved prediction models. The investigator's application is that the simultaneous use of several risk factors (c.f., a single risk factor) will improve the identification of individuals at future risk. The model development proposed in this work will use existing data for body composition and risk factors for cardiovascular and related diseases from the Fels Longitudinal Study. The application points out that the models to be developed are not limited to these data, but can be used in the analyses and interpretation of other longitudinal data sets.