Statistical methods for administrative data

行政数据的统计方法

• 批准号: 227187-2011
• 负责人: Rosychuk, Rhonda
• 金额: $ 0.87万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=478420
• 关键词: Statistical; methods; administrative; data

As a biostatistician, I develop novel statistical methods to improve the design and analysis of studies and work collaboratively on many projects. While my proposal features topics that are motivated by real data and problems related to disease cluster detection, correlated data, and administrative data sources, my methodology could be applied to many other areas in science, engineering, and industry. Disease cluster detection methods are a special type of spatial statistical method that can be used to identify geographic areas with excess cases of disease (called clusters or hot spots). These methods are important for health authorities to be able to monitor populations for diseases in order to target areas for further investigation or intervention. My recent work in this area includes creating a cluster detection method that detects suspected clusters of disease-related events such as emergency department (ED) visits. My proposed research in this area will incorporate time into the cluster detection methods, adapt methods to include errors in administrative data, and determine criteria when rate calculations would agree with detection. The work will include algebraic derivations and manipulations, and computer simulation studies. These new advances will be important for researchers and administrators conducting surveillance of disease or disease-related events, a timely topic with recent pandemic concerns. Other types of correlated data are also of interest for additional research objectives. When multiple responses can be obtained from individuals, like multiple ED visits, the data are correlated and traditional approaches of testing association between two variables will not be valid. I propose to provide new statistical tests for this situation using a resampling method so that strict distributional assumptions are not required. When multiple administrative data sources are combined, and it is desirable to classify subjects into groups, I will investigate principal component analyses. These objectives will also include algebraic derivations and manipulations and computer simulation studies. My research will provide new and valid approaches for assessing associations and classifying individuals into groups, key goals for many applied research projects.

作为一名生物统计学家，我开发了新颖的统计方法，以改善研究的设计和分析，并在许多项目上合作。虽然我的提案的主题是由真实的数据和疾病集群检测相关的问题，相关数据和管理数据源，我的方法可以应用于科学，工程和工业的许多其他领域。疾病聚类检测方法是一种特殊类型的空间统计方法，可用于识别具有过量疾病病例的地理区域（称为聚类或热点）。这些方法对于卫生当局能够监测人口的疾病以便确定进一步调查或干预的目标地区非常重要。我最近在这一领域的工作包括创建一个集群检测方法，检测可疑的疾病相关事件集群，如急诊科（艾德）访问。我在这一领域的研究将把时间纳入集群检测方法，调整方法，包括管理数据中的错误，并确定标准时，率计算将与检测一致。这项工作将包括代数推导和操作，以及计算机模拟研究。这些新的进展将是重要的研究人员和管理人员进行监测的疾病或疾病相关的事件，一个及时的主题与最近的流行病的关注。其他类型的相关数据也对其他研究目标感兴趣。当可以从个人获得多个响应时，如多次艾德访问，数据是相关的，并且测试两个变量之间的关联的传统方法将是无效的。我建议提供新的统计测试，这种情况下使用reservation方法，使严格的分布假设是不需要的。当多个管理数据源相结合，它是可取的主题分类成组，我将研究主成分分析。这些目标也将包括代数推导和操作和计算机模拟研究。我的研究将提供新的和有效的方法来评估协会和个人分类成组，许多应用研究项目的关键目标。