STATISTICAL ANALYSIS OF BODY COMPOSITION & RISK FACTORS

几十年来，统计方法一直被应用于科学研究 以利于对结果的客观解释和判断。 统计方法的进步依赖于激励措施来改善 现有的方法和减少应用的限制。在……里面 生物医学、健康相关和流行病学研究，多重线性 基于最小二乘估计的Logistic回归已得到广泛应用 被采纳，尽管一些统计文献调查了 最小二乘估计在预测变量为 存在相互关联和有影响的观察结果。备择 估计程序已经制定，但在以下方面存在差距 统计学进展及其在健康相关研究中的应用。 参数模型已被应用于与增长相关的序列数据 变量来模拟研究组的个体成长模式。这些 模型假设群体的个人增长模式可以由 一族数学函数和模型中的参数 区分个体差异。尽管如此，对于一些变量，如 随着血压、皮褶厚度和危险因素的增加， 变化如此之大，以至于他们不能由一个 数学函数。在这种情况下，非参数模型被证明是有用的。 计划中的分析包括(1)横断面分析： 预测方程的发展(2)纵向分析： 风险函数的建立及非参数函数的实现 内核回归。将采用最小二乘法来建立方程 根据人体测量和阻抗预测身体成分变量 然后检查诊断措施、多重共线性和 影响观测值及其对最小二乘估计的影响。 岭回归和稳健回归将被用作备选方案。这个 成人价值观与童年价值观的可预测性将会增强 迭代加权最小二乘法在Logistic回归中的应用 或最大似然法。多重共线性的影响和 对Logistic回归估计有影响的观察将是 已评估。岭和健壮的回归将被用作成年期 童年时代的价值观。有限样本调查的一种核估计 将被开发并应用于身体成分的一系列测量和 为风险因素提供平稳的参考数据。 这项首个奖项的长期目标是加强 应用现有的统计方法，开发改进的方法 当遇到现有方法的限制时，从而帮助 研究人员在开发预测方程时，在进行风险 分析，以及在分析长期序列数据时。尽管 将从纵向对现有数据库进行调查 以及对人体生长、身体成分和风险的横断面研究 对于重大疾病的影响因素，这些方法也适用于许多其他疾病 变量。

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.