DEVELOPMENT OF STATISTICAL METHODS FOR PERINATAL DISEASE

围产期疾病统计方法的发展

• 批准号: 2403316
• 负责人: HEPING ZHANG
• 金额: $ 12.02万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-05-01 至 1999-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2403316
• 关键词: computer program /software; congenital disorders; disease /disorder proneness /risk; human data; human morbidity; human mortality; mathematical model; method development; model design /development; perinatal; statistics /biometry

Built upon two advanced nonparametric statistical techniques, Multivariate Adaptive Regression Splines and Classification and Regression Trees, tree-based methods will be developed and applied to explore the data from the Yale Pregnancy Outcome Study (YPOS), which was designed to examine the relationship between pregnancy outcome and a variety of risk factors, including prescription drug and alcohol use, tobacco smoke, caffeine consumption, and contraceptive practice. Data from about 7,000 subjects will be available from two related YPOS's. Analyses will also be extended to several other important databases including the 1988 National Health Interview Survey on Child Health. In contrast to traditional statistical methods and software, the mechanisms that we will employ and investigate have several advantages: (i) automatically finding the important variables and significant interactions among a large number of variables, making it more likely that new risk factors for pregnancy outcome study (other studies as well) will be discovered; (ii) identifying high risk individuals; (iii) efficiently using data by dealing with missing data and predictors of mixed (ordinal, nominal, and nested) types appropriately. We will study pregnancy outcomes associated with perinatal death such as intrauterine growth retardation, small for gestational age, and preterm delivery, and determine the relationship between these outcomes and putative risk factors. Although the YPOS data base has been extensively analyzed using more traditional methods, the tree-based methods will provide a deeper understanding of risk factors, and will therefore impact on the development of plans for public health programs to prevent birth defects. The emphasis of this project will be on interactive effects among risk factors in connection to the outcome of interest (e.g., miscarriage or birthweight). The methods and software developed by this study will offer researchers the opportunity to perform more flexible, realistic, and efficient analyses in epidemiologic studies.

基于两种先进的非参数统计技术， 多元自适应回归样条和分类 回归树，基于树的方法将被开发并应用于 研究耶鲁妊娠结局研究（YPOS）的数据， 旨在研究妊娠结局与 各种风险因素，包括处方药和饮酒， 吸烟、咖啡因摄入和避孕措施。 数据 将从两个相关的YPOS获得约7，000名受试者。 分析还将扩展到其他几个重要的数据库 包括1988年全国儿童健康访谈调查。 与传统的统计方法和软件相比， 我们将采用和研究的机制有几个优点： (i)自动找出重要的变量和重要的 大量变量之间的相互作用，使其更有可能 妊娠结局研究的新风险因素（以及其他研究） （二）发现高危人群;（三） 有效地利用数据，处理缺失数据和 适当地混合（顺序、名义和嵌套）类型。 我们将研究与围产期死亡相关的妊娠结局， 胎儿宫内发育迟缓、小于胎龄儿和早产 交付，并确定这些成果之间的关系， 假定的危险因素。 虽然YPOS数据库已经广泛地 使用更传统的方法进行分析，基于树的方法将 更深入地了解风险因素，因此将影响 制定预防生育的公共卫生方案计划 缺陷 本项目的重点将是风险之间的相互作用 与感兴趣的结果有关的因素（例如，流产或 出生体重）。 本研究开发的方法和软件将 为研究人员提供了更灵活，更现实， 流行病学研究中的有效分析。