Multiple- vs Non- or Single-Imputation based Fuzzy Clustering for Incomplete Longitudinal Behavioral Intervention Data.
Multiple- vs Non- or Single-Imputation based Fuzzy Clustering for Incomplete Longitudinal Behavioral Intervention Data.
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DOI：
10.1109/chase.2016.19
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发表时间：
2016-06
期刊：
影响因子：
--
通讯作者：
Fang H
中科院分区：
文献类型：
--
作者：
Zhang Z;Fang H
解开患者的行为变化是更好地理解干预对个体结果的影响的关键一步。缺失数据在纵向行为干预研究中普遍存在。多重插补（MI）在统计学领域的缺失数据分析中已经得到了很好的研究，但是，尚未对聚类或无监督学习进行详细审查，这是解释治疗效果异质性的重要技术。建立在以前的工作MI模糊聚类，本文从理论上，经验和数值上证明了如何MI为基础的方法可以减少聚类精度的不确定性相比，非和单插补为基础的聚类方法。本文提出了我们的理解的效用和强度的多重插补（MI）为基础的模糊聚类方法处理不完整的纵向行为干预数据。
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.