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

研究了用于评估与包括较大群体的个体的若干小群体中的每一个相关联的属性的方法。 这些小群体可以由较大地理区域内的小地理区域来定义，或者根据年龄等特征将大群体中的个体分类。 要考虑的属性是每个群体中拥有特定特征的个体的数量和比例。 所要调查的方法部分是由一项关于不列颠哥伦比亚省各卫生区（小群体）婴儿死亡率的研究所激发的，其中记录了十年来每个地区的年死亡人数。 这些方法必须承认，数据是由对同一地区的重复观测及其地理邻近性组成的。 特别令人感兴趣的是预测未来几年卫生领域的死亡人数。 拟议的方法还将适用于特定地点和时间的疾病病例（例如麻疹）数据，以期预测流行病的趋势和爆发。 还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，这些方法必须承认比例的界限在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预测在特定时间点每所学校（小团体）吸烟者比例的方法。 要做到这一点，拟议的方法必须承认，数据包括重复的观察随着时间的推移，在同一所学校的学生可能会更相似的吸烟行为比学生就读于不同的学校。 所提出的方法也将被应用于癌症研究的数据，以调查的因素，促使选择手术类型，并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。

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.