Mixed model estimation for discrete longitudinal and/or spatially correlated data from small areas

小区域离散纵向和/或空间相关数据的混合模型估计

• 批准号: 193998-2006
• 负责人: Farrell, Patrick
• 金额: $ 1.38万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2006
• 资助国家: 加拿大
• 起止时间: 2006-01-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=334350
• 关键词: Mixed; model; estimation; discrete; longitudinal

Approaches for assessing attributes associated with each of a number of small groups of individuals comprising a larger group are investigated.  These small groups may be defined by a small geographic area within a larger one, or by classifying individuals in the large group according to some trait such as age.  Attributes to be considered are counts and the proportion of individuals within each group possessing a particular characteristic.    The approaches to be investigated are motivated in part by a study on infant mortality rates in the various health areas (the small groups) in British Columbia, in which the annual number of deaths in each area were recorded over ten years.  These methods must acknowledge that the data consist of repeated observations on the same area, and their geographic proximity.  Of particular interest is the forecasting of mortality counts for health areas in future years.  The proposed methods will also be applied to data on disease cases (measles, for example) at a specific location and time, with a view towards forecasting trends and outbreaks of epidemics.    Methods for studies where the attribute  for each small group is a proportion of individuals that possess a particular characteristic will also be investigated.  Of importance is that the methods must acknowledge that a proportion is bounded between zero and one.  My past research has included an assessment of a seven year smoking prevention study conducted in elementary and high schools.  Of present interest is to consider methods for forecasting the proportion of smokers at each school (the small groups) at specific time points.  To do so, the proposed approaches must acknowledge that the data comprise repeated observations over time, and that students at the same school will likely be more similar regarding smoking behavior than students attending different schools.  The proposed methods will also be applied to data from a cancer study to investigate the factors that motivate the choice of surgery type, and to data reflecting wheezing status in children.    Finally, the proposed methods will also be developed to accommodate missing data; in other words, for situations where the information for the small group over time is incomplete.

研究了评估与众多完成一组较大组的个人相关的属性的方法。这些小组可以由较大的地理区域定义，或根据某些特征（例如年龄）对大组中的个体进行分类。要考虑的属性是每个群体中具有特定特征的个体的比例。要调查的方法部分是由于不列颠哥伦比亚省各个卫生领域（小组）婴儿死亡率的研究而动机，在该研究中，每个地区的年死亡人数在十年内记录了每年的死亡人数。这些方法必须确认数据包括对同一区域的重复观察及其地理位置。特别令人感兴趣的是未来几年对卫生领域的死亡率计数的预测。所提出的方法还将应用于特定位置和时间的疾病病例（例如麻疹）的数据，以预测趋势和发作的暴发。研究方法的方法是每个小组的属性是一个可能特定特征的个体的比例。重要的是，这些方法必须承认一个比例在零和一个之间。我过去的研究包括对在小学和高中进行的七年预防吸烟研究的评估。目前感兴趣的是考虑在特定时间点预测每个学校（小组）吸烟者比例的方法。为此，拟议的方法必须承认，随着时间的流逝，数据包括反复观察，并且同一学校的学生在吸烟行为上可能比上学不同学校更相似。所提出的方法还将应用于癌症研究的数据，以调查激励手术类型选择的因素，以及反映儿童旋转状态的数据。最后，还将开发提出的方法以适应丢失的数据。换句话说，对于小组随着时间的信息的信息而言，不完整的情况。