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: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=392370
• 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.

研究了与组成一个较大群体的许多小群体中的每一个相关的属性的评估方法。这些小群体可以由较大群体中的小地理区域来定义，或者根据某些特征（如年龄）对大群体中的个体进行分类。要考虑的属性是每个群体中具有特定特征的个体的数量和比例。 拟调查的方法部分是由一项关于各卫生领域婴儿死亡率的研究所推动的（小团体）在不列颠哥伦比亚省，其中每年的死亡人数在每个地区记录了十年以上。这些方法必须承认，数据包括重复观察同一地区，特别值得关注的是预测未来几年健康地区的死亡人数。所提出的方法也将应用于疾病病例数据（例如麻疹）的数据，以便预测流行病的趋势和爆发。 也将研究每个小组的属性是具有特定特征的个体的比例的研究方法。重要的是，方法必须承认比例在0和1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以下方法：预测每所学校的吸烟者比例要做到这一点，拟议的方法必须承认，数据包括随着时间的推移重复观察，同一所学校的学生在吸烟行为方面可能比不同学校的学生更相似。所提出的方法也将应用于癌症研究的数据，促使选择手术类型的因素，以及反映儿童喘息状态的数据。 最后，还将制定拟议的方法，以适应缺失的数据;换句话说，对于小群体的信息随着时间的推移是不完整的情况。