标题： BAREB：一种用于牙周数据的贝叶斯排斥双聚类模型 显示英文标题

标题： BAREB: A Bayesian repulsive biclustering model for periodontal data

摘要： 预防牙周病（PD）并保持牙齿的结构和功能是个人口腔护理的重要目标。 为了了解具有不同PD模式的患者的异质性，我们开发了BAREB，这是一种贝叶斯排斥双聚类方法，在考虑患者和位点层面的协变量后，可以同时对PD患者及其牙齿位点进行聚类。 BAREB使用行列式点过程（DPP）先验，以在不同的双聚类之间引入多样性，从而促进简洁性和可解释性。 由于PD的进展被假定为具有空间相关性，BAREB考虑了牙齿位点之间的空间依赖性。 此外，由于PD是牙齿脱落的主要原因，缺失数据机制是不可忽略的。 这种非随机缺失性被纳入BAREB中。 对于后验推断，我们设计了一个高效的可逆跳跃马尔可夫链蒙特卡洛采样器。 模拟研究表明，BAREB能够准确估计双聚类，并且优于其他方法。 在实际应用中，我们将BAREB应用于一项临床PD研究的数据集，并获得了理想且可解释的结果。 本文的一个主要贡献是BAREB的Rcpp实现，可在https://github.com/YanxunXu/BAREB获取。 显示英文摘要

摘要： Preventing periodontal diseases (PD) and maintaining the structure and function of teeth are important goals for personal oral care. To understand the heterogeneity in patients with diverse PD patterns, we develop BAREB, a Bayesian repulsive biclustering method that can simultaneously cluster the PD patients and their tooth sites after taking the patient- and site- level covariates into consideration. BAREB uses the determinantal point process (DPP) prior to induce diversity among different biclusters to facilitate parsimony and interpretability. Since PD progression is hypothesized to be spatially-referenced, BAREB factors in the spatial dependence among tooth sites. In addition, since PD is the leading cause for tooth loss, the missing data mechanism is non-ignorable. Such nonrandom missingness is incorporated into BAREB. For the posterior inference, we design an efficient reversible jump Markov chain Monte Carlo sampler. Simulation studies show that BAREB is able to accurately estimate the biclusters, and compares favorably to alternatives. For real world application, we apply BAREB to a dataset from a clinical PD study, and obtain desirable and interpretable results. A major contribution of this paper is the Rcpp implementation of BAREB, available at https://github.com/YanxunXu/ BAREB.