STATISTICAL ANALYSIS OF BODY COMPOSITION & RISK FACTORS

身体成分统计分析

• 批准号: 3470273
• 负责人: SHUMEI S SUN
• 金额: $ 9.52万
• 依托单位: WRIGHT STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-04-01 至 1995-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3470273
• 关键词: body composition; body physical characteristic; cardiovascular disorder epidemiology; child physical development; disease /disorder proneness /risk; electrical impedance; epidemiology; hypertension; longitudinal human study; mathematical model; statistics /biometry

For decades, statistical methods have been applied in scientific research to facilitate objective interpretation and judgment of the results. Progress in statistical methods relies on incentives to improve the existing methods and to reduce the limitations of applications. In biomedical, health-related, and epidemiological studies, multiple linear and logistic regressions with least square estimation have been widely adopted, despite the fact that some statistical literature has investigated the limitations of least square estimation when predictor variables are interrelated and influential observations are present. Alternative estimation procedures have been developed, but there is a gap between statistical advances and their application to health-related research. Parametric models have been applied to serial data of growth-related variables to simulate individual growth patterns for a study group. These models assume individual growth pattern for the group can be represented by a family of mathematical functions and the parameters in the models differentiate individual differences. Nonetheless, for some variables such as blood pressures, skinfold thicknesses, and risk factors, the patterns of change vary so much that they cannot be represented by a family of mathematical functions. In this case, nonparametric models prove useful. The planned analyses encompass (1) cross-sectional analyses: the development of predictive equations (2) longitudinal analyses: the establishment of risk functions, and the implementation of nonparametric kernel regression. Least squares will be adopted to formulate equation to predict body composition variables from anthropometry and impedance followed by examination of diagnostic measures, multicollinearity and influential observations and their effects on least square estimation. Ridge and robust regressions will be used as alternatives. The predictability of adulthood values from childhood values will e investigated by logistic regression using iterative weighted least squares or maximum likelihood methods. The effects of multicollinearity and influential observations on the logistic regression estimates will be evaluated. Ridge and robust regressions will be employed as adulthood to values in childhood. A kernel estimator for finite sample investigation will be developed and applied to serial measures of body composition and risk factors to provide smooth reference data. The long-term goal of this First Award proposal are to enhance the application of existing statistical methods, to develop improved methods when limitations of existing methods are encountered, and thus to assist investigators int he development of predictive equations, in making risk analyses, and in analyzing long-term serial data. Although the investigation will be performed on existing data bases from longitudinal and cross-sectional studies of human growth, body composition, and risk factors for major diseases, these methods are applicable to many other variables.

几十年来，统计方法一直应用于科学研究 以便对结果进行客观的解释和判断。 统计方法的进步依赖于改进统计方法的激励措施。 现有的方法，并减少应用的限制。 在 生物医学、健康相关和流行病学研究，多重线性 最小二乘估计的Logistic回归已被广泛应用于 尽管一些统计文献已经调查了 最小二乘估计的局限性，当预测变量是 提出了相互关联和有影响的意见。 替代 估计程序已经制定，但有一个差距 统计进展及其在卫生相关研究中的应用。 参数模型已被应用到与生长相关的序列数据， 变量来模拟研究组的个体生长模式。 这些 模型假设群体的个体增长模式可以表示为 数学函数族和模型中的参数 区分个体差异。 尽管如此，对于某些变量， 作为血压，皮褶厚度和风险因素， 变化如此之大，以至于不能用一个家庭来代表。 数学函数 在这种情况下，非参数模型证明是有用的。 计划的分析包括（1）横断面分析： 预测方程的发展（2）纵向分析： 风险函数的建立和非参数的实现 核回归 将采用最小二乘法公式化， 根据人体测量和阻抗预测身体成分变量 其次是检查诊断措施，多重共线性， 有影响的观测及其对最小二乘估计的影响。 岭回归和稳健回归将被用作替代方法。 的 从童年价值观中预测成年价值观将是一个很好的方法。 使用迭代加权最小二乘法进行逻辑回归研究 或最大似然方法。 多重共线性的影响， 对logistic回归估计值有影响的观察结果将是 评估。 岭回归和稳健回归将被用作成年期， 童年的价值观 有限样本调查的核估计 将被开发并应用于身体成分的系列测量， 风险因素，以提供平稳的参考数据。 这个第一个奖项提案的长期目标是提高 应用现有统计方法，开发改进方法 当遇到现有方法的局限性时， 研究人员在开发预测方程时， 分析，并在分析长期的串行数据。 虽然 调查将在现有数据库上进行， 以及人类生长、身体组成和风险的横断面研究 主要疾病的因素，这些方法适用于许多其他 变量