STATISTICAL ANALYSIS OF BODY COMPOSITION RISK FACTORS

身体成分风险因素统计分析

• 批准号: 6329890
• 负责人: SHUMEI S SUN
• 金额: $ 19.89万
• 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 2002-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6329890
• 关键词: body composition; body weight; cardiovascular disorder epidemiology; child physical development; clinical research; computer program /software; data collection methodology /evaluation; disease /disorder proneness /risk; human data; hypertension; longitudinal human study; mathematical model; model design /development; statistics /biometry

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.

描述：(改编自申请人的描述)：建议 研究是对已完成工作的延续和扩展 由该申请人在当前项目中提交。总体目标是应用 人体研究的最新、先进的统计方法 心血管疾病和相关疾病的构成和风险因素。 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被用来测试 先进统计方法的适用性。两个具体的 本应用程序的目的是：1)确定统计模型或 可以使用调整来说明测量的影响 纵向研究中关于回归估计的错误， 假设检验和模型预测；测量误差可能 减少因变量和自变量之间的关系 导致回归估计的偏差，降低了 假设检验，并降低了预测的概括性 未来风险；以及2)评估多个风险同时使用的情况 多变量、纵向模型中改善预测的因素 对未来风险的担忧。 这些拟议的统计改进是由以下研究推动的 心血管疾病的长期系列危险因素和 这些研究中引入的测量误差所产生的问题， 尤其是那些与身体成分有关的错误 测量。测量误差对独立性的影响 将检查和调整纵向模型上的变量。 根据早先的值准确预测未来值的能力， 例如在儿童时期，在许多纵向研究中是重要的，并且 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，因此对 风险因素之间的多变量关系应该有助于 多变量之间关系的推导和澄清， 并考虑到改进的预测模型。调查员的 应用是同时使用几个风险因素(参见， 单一风险因素)将改善对个人的识别 冒着未来的风险。 这项工作中提出的模型开发将使用现有数据来 心血管疾病及相关疾病的身体成分和危险因素 来自FELS纵向研究的疾病。该应用程序指出 要开发的模型不仅限于这些数据，而且可以 可用于其他纵向资料的分析和解释 布景。