In longitudinal studies in which a medical device is used to monitor outcome repeatedly and frequently on the same patients over a prespecified duration of time, two clustering goals can arise. One goal is to assess the degree of heterogeneity among patient profiles. A second yet equally important goal unique to such studies is to determine frequency and duration of monitoring sufficient to identify longitudinal changes. Considering these goals jointly would identify clusters of patients who share similar patterns over time and characterize temporal stability within each cluster. We use a biclustering approach, allowing simultaneous clustering of observations at both patient and time levels and using a nonparametric hierarchical Bayesian model. Because clustering units at the time level (i.e., time points) are ordered and hence unexchangeable, we utilize a multivariate Dirichlet process mixture model by specifying a Dirichlet process prior at the patient level whose base measure employs change points at the time level to achieve the desired joint clustering. We consider structured covariance between consecutive time points and assess model performance through simulation studies. We apply the model to data on 24‐hr ambulatory blood pressure monitoring and examine the relationship between diastolic blood pressure and pediatric obstructive sleep apnoea.

中文翻译：

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使用非参数分层贝叶斯模型对医疗监测数据进行双聚类

在纵向研究中，使用医疗设备在预先指定的时间段内反复频繁地监测同一患者的结果，可能会出现两个聚类目标。一个目标是评估患者档案之间的异质性程度。此类研究特有的第二个同样重要的目标是确定足以识别纵向变化的监测频率和持续时间。共同考虑这些目标将确定随着时间的推移具有相似模式的患者集群，并表征每个集群内的时间稳定性。我们使用双聚类方法，允许同时对患者和时间级别的观察结果进行聚类，并使用非参数分层贝叶斯模型。因为时间级别（即时间点）的聚类单元是有序的，因此不可交换，我们通过在患者级别指定 Dirichlet 过程先验来利用多变量 Dirichlet 过程混合模型，其基本度量采用时间级别的变化点来实现所需的联合聚类。我们考虑连续时间点之间的结构化协方差，并通过模拟研究评估模型性能。我们将该模型应用于 24 小时动态血压监测数据，并检查舒张压与小儿阻塞性睡眠呼吸暂停之间的关系。