CORE--DATA MANAGEMENT & STATISTICS & COMMUNITY IMPACT MODELING

数据管理、统计和社区影响建模(DSM) 核心构成了所有统计和管理的中心资源 需要。其他每个核心和研究项目产生的数据 将被转移到数据库管理股以存储在 集成的SIR/DBMS数据管理系统，旨在满足所有需求 中心在清理、组织和检索数据方面的作用 用于计划分析和临时分析。这些活动将由 在联网系统上工作的数据管理员和数据录入员 专门为数据管理操作配置的奔腾PC，在 核心董事的直接监督。 统计股将负责协调规划和 实施研究中心进行的所有分析，统计 和编程人员。建议的研究项目有一定的 具有相似分析方法的共同特征。 其中包括一个共享的预期队列，纵向的集合 数据、对基准值更改的兴趣以及以下各项的组合 定义哮喘和不良反应的二分和连续变量 发展成果衡量标准。 除了这些传统的核心功能外，社区的影响 在需求侧管理核心中加入了建模单元，以：(1)提供研究 具有集中社区信息资源的项目；(2) 在制定社区风险量化措施方面提供专业知识； (3)将这些措施纳入具体项目的统计模型，以 在控制了个别风险因素后，确定是否不利 儿童的健康结果与社区层面的风险有关；(4) 确定个人风险和不利因素之间的关系 健康结果因社区风险而异，(5) 确定计划的干预措施的效果是否受到 社区层面的风险。

The Data Management and Statistics and Community Impact Modeling (DSM) Core constitutes the central resource for all statistical, and management needs. Data generated by each of the other cores and research projects will be transferred to the Database Management Unit for storage in an integrated SIR/DBMS data management system designed to meet all the needs of the Center with respect to cleaning, organization and retrieval of data for planned and ad hoc analyses. These activities will be carried out by a data manager and a data entry clerk working on a networked system of Pentium PCs specifically configured for data management operations, under the direct supervision of the Core Director. The Statistics Unit will be responsible for coordinating the planning and implementation of all analyses conducted by Center research, statistical and programming staff. The proposed research projects have certain features in common that lend themselves tot similar analytic methods. These include a shared prospective cohort, the collection of longitudinal data, interest in change from baseline values, and a combination of dichotomous and continuous variables that defined the asthma and adverse developmental outcome measures. In addition to these traditional core functions, a Community Impact Modeling Unit is included in the DSM core to: (1) provide the research projects with a centralized resource for community-level information; (2) provide expertise in developing quantitative measures of community risk; (3) incorporate these measures into project-specific statistical models to determine whether, after controlling for individual risk factors, adverse health outcomes of children are related to community level risk; (4) determine whether relationships between individual risks and adverse health outcomes vary as a function of community level risk and (5) establish whether the effects of the planned interventions are affected by community level risk.