Analyzing unequally spaced familial-longitudinal and familial-spatial data

分析不等距的家族纵向和家族空间数据

• 批准号: RGPIN-2019-05694
• 负责人: Oyet, Alwell
• 金额: $ 1.17万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738456
• 关键词: Analyzing; unequally; spaced; familial; longitudinal

In longitudinal studies, a small number of repeated observations, along with associated covariates are collected over time from a large number of experimental/family units. As an example, many researchers have studied repeated seizure counts of 59 individuals with epilepsy as a function of various covariates such as age and baseline seizure rate, amongst others. In order to accommodate the correlations among the repeated count data, researchers have used dynamic models for equally spaced Poisson counts to study the repeated seizure counts. Clearly, seizures can occur at irregularly spaced time intervals for members of the same family which may lead to irregularly spaced repeated counts for some individuals in the family. Unequally spaced familial-longitudinal (FL) responses can also occur due to the design of an investigation, unequally spaced appointments and public holidays or weekends between measurements. We note that, in general, unequally spaced FL measurements can be binary (e.g. asthma status), continuous (e.g. household (HH) debt), or counts (number of physician visits).  To the best of our knowledge, the development of methods for analysis of unequally spaced FL data have not been adequately addressed because of the complicated correlation between unequally spaced repeated responses. Therefore, in the first part of this research, we will develop and study dynamic models that will take into account the complicated structure of the correlation between responses in FL continuous, count and binary data. A second objective of our program of research will be the analysis of multivariate responses, along with multidimensional covariates that are collected from a large number of spatial locations. For instance, in a national study on HH debt, one may collect multivariate responses from a large number of HHs at different spatial locations along with covariate information such as value of dwelling, HH size and HH income, amongst others. Clearly, the multivariate responses from each HH and the responses from HHs in the same neighborhood are likely to be correlated. HHs within a fixed distance from a central location will then naturally constitute a cluster of correlated locations. It is also possible for some HHs to belong to more than one cluster. These HHs will then induce a moving spatial correlation in the data. Our objective is to develop and test models for the analysis of multivariate/familial-spatial (FS) data, that will take into account the moving-cluster based spatial correlation between observations at neighboring locations and also account for the familial correlation between responses from the same location. We will examine the performance of our methods through an intensive simulation study and also demonstrate how the methods can be applied to real data. Masters and doctorate students, representing diverse backgrounds and gender equity, are expected to be trained and contribute to the research during their training.

在纵向研究中，随着时间的推移，从大量的实验/家庭单位收集了少量的重复观察以及相关的协变量。例如，许多研究人员研究了59名癫痫患者的重复癫痫发作计数，作为各种协变量的函数，如年龄和基线癫痫发作率等。为了适应重复计数数据之间的相关性，研究人员使用了等间距泊松计数的动态模型来研究重复发作计数。显然，对于同一家庭的成员，癫痫发作可能以不规则的间隔时间间隔发生，这可能导致家庭中的一些个体出现不规则间隔的重复计数。不等间隔的家庭-纵向(FL)反应也可能发生，这是由于调查的设计、不等间隔的预约和两次测量之间的公共节假日或周末。我们注意到，一般来说，不等间隔的FL测量可以是二元的(例如哮喘状态)、连续的(例如家庭(HH)债务)或计数(就诊次数)。据我们所知，由于不等间隔的重复响应之间的复杂关联，不等间隔的FL数据的分析方法的开发还没有得到充分的解决。因此，在本研究的第一部分，我们将开发和研究动态模型，该模型将考虑到FL连续数据、计数数据和二进制数据中反应之间关联的复杂结构。我们研究计划的第二个目标将是分析多变量响应，以及从大量空间位置收集的多维协变量。例如，在一项关于HH债务的全国性研究中，人们可以从不同空间位置的大量HH收集多变量响应，以及协变量信息，如住宅价值、HH规模和HH收入等。显然，来自每个HH的多变量响应与来自同一社区HHS的响应可能是相关的。然后，距离中心位置固定距离内的HHS将自然地构成相关位置的集群。某些HHS也可能属于多个集群。然后，这些HHS将在数据中引入移动的空间相关性。我们的目标是开发和测试多变量/家庭空间(FS)数据的分析模型，该模型将考虑相邻位置的观测之间基于移动聚类的空间相关性，并考虑来自相同位置的响应之间的家庭相关性。我们将通过密集的模拟研究来检验我们的方法的性能，并演示如何将这些方法应用于真实数据。代表不同背景和性别平等的硕士和博士生将接受培训，并在培训期间为研究做出贡献。