基于多重插补与非插补或单重插补的模糊聚类方法处理不完全纵向行为干预数据
Multiple- vs Non- or Single-Imputation based Fuzzy Clustering for Incomplete Longitudinal Behavioral Intervention Data.
作者信息
Zhang Zhaoyang, Fang Hua
机构信息
Division of Biostatistics and Health Services Research, Department of Quantitative Health Science, University of Massachusetts Medical School, Worcester, MA 01655.
出版信息
IEEE Int Conf Connect Health Appl Syst Eng Technol. 2016 Jun;2016:219-228. doi: 10.1109/CHASE.2016.19. Epub 2016 Aug 18.
Abstract
Disentangling patients' behavioral variations is a critical step for better understanding an intervention's effects on individual outcomes. Missing data commonly exist in longitudinal behavioral intervention studies. Multiple imputation (MI) has been well studied for missing data analyses in the statistical field, however, has not yet been scrutinized for clustering or unsupervised learning, which are important techniques for explaining the heterogeneity of treatment effects. Built upon previous work on MI fuzzy clustering, this paper theoretically, empirically and numerically demonstrate how MI-based approach can reduce the uncertainty of clustering accuracy in comparison to non-and single-imputation based clustering approach. This paper advances our understanding of the utility and strength of multiple-imputation (MI) based fuzzy clustering approach to processing incomplete longitudinal behavioral intervention data.
摘要
理清患者的行为变化是更好地理解干预对个体结果影响的关键一步。纵向行为干预研究中普遍存在缺失数据。多重填补（MI）在统计领域的缺失数据分析中已得到充分研究，然而，尚未针对聚类或无监督学习进行审查，而聚类和无监督学习是解释治疗效果异质性的重要技术。基于先前关于MI模糊聚类的工作，本文从理论、实证和数值方面证明了与基于非填补和单一填补的聚类方法相比，基于MI的方法如何能够降低聚类准确性的不确定性。本文增进了我们对基于多重填补（MI）的模糊聚类方法在处理不完整纵向行为干预数据方面的效用和优势的理解。
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