| 王孝焱,孙秀彬,纪伊曼,张涛,刘云霞.群组多轨迹模型在纵向数据研究中的应用及实例分析[J].Chinese journal of Epidemiology,2024,45(11):1590-1597 |
| 群组多轨迹模型在纵向数据研究中的应用及实例分析 |
| Application and case study of group-based multi-trajectory model in longitudinal data research |
| Received:May 29, 2024 |
| DOI：10.3760/cma.j.cn112338-20240529-00314 |
| KeyWord: 纵向数据 群组多轨迹模型 发展轨迹 队列研究 |
| English Key Word: Longitudinal data Group-based multi-trajectory model Developmental trajectory Cohort study |
| FundProject:国家重点研发计划（2021YFF0704101）；国家自然科学基金（82222064）；基于移动设备的老年人群生活方式干预研究（21320012002309） |
| Author Name | Affiliation | E-mail | | Wang Xiaoyan | Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
Institute for Medical Dataology, Shandong University, Jinan 250002, China | | | Sun Xiubin | Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
Institute for Medical Dataology, Shandong University, Jinan 250002, China | | | Ji Yiman | Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
Institute for Medical Dataology, Shandong University, Jinan 250002, China | | | Zhang Tao | Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
Institute for Medical Dataology, Shandong University, Jinan 250002, China | | | Liu Yunxia | Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
Institute for Medical Dataology, Shandong University, Jinan 250002, China | yunxialiu@163.com |
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| Abstract: |
| 纵向队列的发展为识别和监测影响疾病病程或健康状况的多种生物标志物及行为等因素创造了条件。然而，传统统计方法通常只能利用单变量纵向数据的信息进行研究，无法充分利用多变量纵向数据信息。群组多轨迹模型（GBMTM）是近年来提出的研究多变量发展轨迹的一种方法，通过影响目标结局的多个指标来识别遵循相似轨迹的潜在人群亚组，在处理多变量纵向数据中具有独特优势。本研究阐述GBMTM的基本原理，并运用一项基于智能穿戴设备的老年人健康管理研究的数据探索多种生活相关指标与高血压的关系，展示GBMTM的具体应用，以期促进其在纵向队列研究中的应用。 |
| English Abstract: |
| The development of longitudinal cohorts has made the identification and surveillance of multiple biological markers and behavioral factors which influence disease course or health status become possible. However, traditional statistical methods typically use univariate longitudinal data for research, failing to fully exploit the information from multivariate longitudinal data. The group-based multi-trajectory model (GBMTM) emerged as a method to study the developmental trajectory of multivariate data in recent years. GBMTM has distinct advantages in analyzing multivariate longitudinal data by identifying potential subgroups of populations following similar trajectories by multiple indicators that influence the outcome of interest. In this study, we introduced the application of GBMTM by explaining the fundamental principles and using the data from a health management study in the elderly by using smart wearing equipment to investigate the relationship between multiple life-related variables and hypertension to promote the wider use of GBMTM in longitudinal cohort studies. |
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