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 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 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 nonignorable. 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 article is the Rcpp implementation of our methodology, available in the R package BAREB.

中文翻译：

---

BAREB：用于牙周数据的贝叶斯排斥双聚类模型。

预防牙周疾病（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，我们开发了一种贝叶斯排斥双聚类方法，该方法可以在考虑患者和部位水平的协变量后同时聚类PD患者及其牙齿部位。BAREB在先行确定性点过程之后，才在不同的两类群体之间引起多样性，以促进简约性和可解释性。由于PD进展被假定为空间参考，因此BAREB会影响牙齿部位之间的空间依赖性。此外，由于PD是造成牙齿脱落的主要原因，因此丢失数据机制是不可忽略的。这种非随机的缺失被合并到BAREB中。对于后验推论，我们设计了一个有效的可逆跳跃马尔可夫链蒙特卡洛采样器。仿真研究表明，BAREB能够准确估计双峰，并与其他方案相比具有优势。对于实际应用，我们将BAREB应用于临床PD研究的数据集，并获得理想的和可解释的结果。本文的主要贡献是我们的方法的Rcpp实现，可在R包BAREB中获得。