Statistical methods for multilevel data with informative cluster size

具有信息簇大小的多级数据的统计方法

• 批准号: RGPIN-2022-05356
• 负责人: Mitani, Aya
• 金额: $ 1.38万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764378
• 关键词: Statistical; methods; multilevel; data; informative

Large-scale clustered longitudinal studies are conducted more frequently now, where data on a unit that belongs to a cluster is obtained for more than two time points. Informative cluster size occurs when the sizes of the clusters vary and are related to the outcome of interest. For example, in a longitudinal study of periodontal disease, where a unit is a tooth and a cluster is a person, each person's oral health status is measured at multiple timepoints. Because severe periodontal disease can lead to tooth loss, the outcome of interest, time to disease progression, is related to cluster size (number of teeth within a person); a tooth that belongs to a person with few teeth is more likely to experience the outcome than a tooth that belong to a person with a full set of teeth. In this case, taking the average of all the teeth measurements of all the people over time will underestimate the rate of disease. Statistical methods that account for informative cluster size are limited for longitudinal data. The proposed research includes three objectives that will develop new methods and tools to appropriately analyze complex longitudinal data. 1) A unit that belongs to a cluster can go through various stages of disease or status in life, but in some situations, time to transition from one state to another can be related to cluster size. Therefore, we propose to develop a clustered multistate model that accounts for informative cluster size. We will create a flexible software package that incorporates weights in clustered multistate models. 2) In a longitudinal study, we can estimate the probability of a certain event based on the longitudinal measurements made on each unit. We will extend two dynamic prediction models - joint modelling and landmarking - to the clustered data setting. The proposed clustered dynamic prediction models will properly account for the correlation between units within a cluster to make accurate cluster-specific and population-averaged predictions. 3) Missing data is unavoidable in large-scale and long-term longitudinal studies. We will evaluate various multiple imputation approaches to impute missing outcomes from clustered longitudinal data with informative cluster size. We will also investigate the case when the missing data pattern is related to cluster size. The results from each objective will be disseminated as journal articles accompanied by software packages or code for analysis and conference presentations. The proposed methods have applications in many areas of research: examples include a multi-center longitudinal study of arthritis patients and a multi-school longitudinal study of children and adolescents. Our proposed research will strengthen the field of complex correlated data analysis and provide researchers with the appropriate methods and tools to analyze data, translate knowledge, and create policies that will improve the health and well-being of Canadians.

现在更频繁地进行大规模的集群式纵向研究，其中属于一个集群的单位的数据被获得两个以上的时间点。当簇的大小变化并且与感兴趣的结果相关时，信息性簇大小就会发生。例如，在牙周病的纵向研究中，单位是牙齿，簇是人，每个人的口腔健康状况是在多个时间点测量的。因为严重的牙周病可能会导致牙齿脱落，所以相关的结果，疾病进展的时间，与牙丛大小(一个人的牙齿数量)有关；属于牙齿稀少的人的牙齿比属于拥有完整牙齿的人的牙齿更有可能经历这种结果。在这种情况下，取所有人在一段时间内所有牙齿尺寸的平均值将低估患病率。对于纵向数据，说明信息性集群大小的统计方法是有限的。拟议的研究包括三个目标，这三个目标将开发新的方法和工具来适当地分析复杂的纵向数据。1)属于集群的一个单元可能会经历疾病或生命状态的不同阶段，但在某些情况下，从一种状态转换到另一种状态的时间可能与集群的大小有关。因此，我们建议开发一个集群多态模型，该模型可以解释信息性的集群大小。我们将创建一个灵活的软件包，其中包含集群多态模型中的权重。2)在纵向研究中，我们可以根据对每个单元进行的纵向测量来估计某一事件的概率。我们将把两个动态预测模型--联合建模和里程碑--扩展到集群数据设置。建议的集群动态预测模型将适当地考虑集群内单元之间的相关性，以做出准确的集群特定预测和总体平均预测。3)在大规模、长期的纵向研究中，数据缺失是不可避免的。我们将评估各种多重归因方法，以归因于从具有信息的聚类大小的聚类纵向数据中归因于缺失结果的各种方法。我们还将调查丢失数据模式与集群大小相关的情况。每项目标的成果将以期刊文章的形式散发，并附有用于分析和会议介绍的软件包或代码。所提出的方法在许多研究领域都有应用：例如对关节炎患者的多中心纵向研究和对儿童和青少年的多学校纵向研究。我们拟议的研究将加强复杂的相关数据分析领域，并为研究人员提供适当的方法和工具来分析数据、转化知识和制定政策，以改善加拿大人的健康和福祉。