METHODS FOR ANALYZING LONGITUDINAL DATA

纵向数据分析方法

• 批准号: 3222607
• 负责人: CHARLES J KOWALSKI
• 金额: $ 8.47万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-07-01 至 1991-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3222607
• 关键词: cephalometry; child (0-11); cleft lip; cleft palate; computer assisted medical decision making; computer data analysis; computer simulation; developmental neurobiology; face; growth /development; longitudinal human study; mathematical model; oral pharyngeal surgery; orthodontics; statistics /biometry

It is generally acknowledged that longitudinal rather than cross- sectional data provide the most information on growth and other time-dependent phenomena. This recognition in the areas of craniofacial biology has led to the collection of excellent longitudinal cephalometric data sets that contain high quality information on normal, abnormal and altered growth and development in humans and other primates. Nevertheless, with the exception of a few studies (Dawson et al. 1980; Schneiderman, 1985; Buschang, et al., 1986), none use appropriate methods of analysis that adequately account for the covariance structure of such data sets. Apart from two published computer programs (Schneiderman and Kowalski, 1985, 1987) which perform Rao's single sample polynomial growth curve analysis and Hills procedures for unequal-time intervals, methods that are accessible and readily usable by the biomedical community are unavailable. Despite the formulation of models which are suitable for the analysis of longitudinal data (see review in Kowalski and Guire, 1974; Marubini and Milani, 1986), implementation has been thwarted due to computational complexity. Since these methods have been used with substantial data sets, their formal properties remain largely unknown. The purpose of this research is to systematically compare available methods and implement the most suitable ones using the new matrix algebra programming language, GAUSS. The goal is to develop computer programs that can be used easily by basic and clinical scientists to facilitate meaningful descriptions and comparisons of growth patterns in human and other primate populations. The current widespread use of conventional statistics (based on ordinary least squares) has resulted in misleading standards that may underestimate variability. Specific problems to be addressed are (1) treatment of missing data. (2) multigroup comparisons, (3) unbalanced designs, (4) multivariate situations and (5) the formal properties of the methods implemented such as robustness, precision, power and efficiency. Simulations will be used to investigate these properties. An integrated system of use friendly programs that performs the most useful methods will then be used to reanalyze The University of Michigan's Elementary School Children data as well as normative rhesus monkey data. Articles issuing from this project will be written for investigators with only a basic understanding of statistics and will describe the conceptual basis, practical applications and use of the methods and programs. By providing easy-to-use tools for generating accurate descriptions of human growth, this project will contribute to improved health care in the areas of orthodontics, oral surgery, cleft-lip and palate rehabilitation, pediatric neurosurgery.

人们普遍认为，纵向而不是交叉 部分数据提供了有关增长和其他的最多信息 时间依赖现象。 在该领域的这种认可 颅面生物学导致了优秀的收集 包含高质量的纵向头部测量数据集 有关正常，异常和变化的信息 人类和其他灵长类动物的发展。 尽管如此， 除一些研究外（Dawson等，1980; Schneiderman， 1985; Buschang等，1986），没有使用适当的方法 充分说明的分析 这样的数据集。 除了两个已发布的计算机程序 （Schneiderman和Kowalski，1985，1987） 单样品多项式生长曲线分析和丘陵 不等时间间隔的程序，方法 生物医学社区可访问且容易使用 不可用。 尽管制定了合适的模型 有关纵向数据的分析（请参阅Kowalski和 Guire，1974年； Marubini和Milani，1986年），实施 由于计算复杂性而受挫。 由于这些方法 已与大量数据集一起使用，其正式属性 在很大程度上未知。 这项研究的目的是系统地比较 可用方法并使用最合适的方法 新矩阵代数编程语言，高斯。 目标是 开发可以轻松使用基本的计算机程序 和临床科学家促进有意义的描述和 比较人和其他灵长类动物中生长模式 人群。 当前广泛使用常规 统计（基于普通最小二乘）导致 误导标准可能会低估可变性。 具体的 要解决的问题是（1）丢失数据的处理。 （2） 多组比较，（3）不平衡的设计，（4）多变量 情况和（5）方法的形式特性 实施，例如鲁棒性，精度，功率和效率。 模拟将用于研究这些属性。 一个 执行的集成使用友好程序系统 然后，最有用的方法将用于重新分析大学 密歇根州的小学儿童数据以及 规范性恒河猴数据。 该项目发行的文章 将为调查人员写作，只有基本的理解 统计数据，将描述概念基础，实用 方法和程序的应用和使用。 通过提供 易于使用的工具，用于生成人类的准确描述 增长，该项目将有助于改善医疗保健 正畸，口腔手术，唇裂和味觉的区域 康复，小儿神经外科。