INFORMATION-INCORPORATED CLUSTERING ANALYSIS OF DISEASE PREVALENCE TRENDS

• 成果类型: Article
• 署名作者: Ma, Chenjin; Lin, Cunjie; Xue, Yuan; Zhang, Sanguo; Zhang, Qingzhao; Ma, Shuangge
• 署名单位: Beijing University of Technology; Renmin University of China; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Xiamen University; Yale University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1821
• 发表日期: 2024
• 页码: 1035-1050
• 关键词: pathogenesis

摘要

In biomedical research the analysis of disease prevalence is of critical importance. While most of the existing prevalence studies focus on individual diseases, there has been increasing effort that jointly examines the prevalence values and their trends of multiple diseases. Such joint analysis can provide valuable insights not shared by individual -disease analysis. A critical limitation of the existing analysis is that there is a lack of attention to existing information, which has been accumulated through a large number of studies and can be valuable especially when there are a large number of diseases but the number of prevalence values for a specific disease is limited. In this study we conduct the functional clustering analysis of prevalence trends for a large number of diseases. A novel approach based on the penalized fusion technique is developed to incorporate information mined from published articles. It is innovatively designed to take into account that such information may not be fully relevant or correct. Another significant development is that statistical properties are rigorously established. Simulation is conducted and demonstrates its competitive performance. In the analysis of data from Taiwan NHIRD (National Health Insurance Research Database), new and interesting findings that differ from the existing ones are made.