Guo Wensheng, You Mengying, Yi Jialin, Pontari Michel A, Landis J Richard
Department of Urology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19104 U.S.A.
Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104 U.S.A.
J Am Stat Assoc. 2022;117(540):1631-1641. doi: 10.1080/01621459.2022.2066536. Epub 2022 May 13.
By clustering patients with the urologic chronic pelvic pain syndromes (UCPPS) into homogeneous subgroups and associating these subgroups with baseline covariates and other clinical outcomes, we provide opportunities to investigate different potential elements of pathogenesis, which may also guide us in selection of appropriate therapeutic targets. Motivated by the longitudinal urologic symptom data with extensive subject heterogeneity and differential variability of trajectories, we propose a functional clustering procedure where each subgroup is modeled by a functional mixed effects model, and the posterior probability is used to iteratively classify each subject into different subgroups. The classification takes into account both group-average trajectories and between-subject variabilities. We develop an equivalent state-space model for efficient computation. We also propose a cross-validation based Kullback-Leibler information criterion to choose the optimal number of subgroups. The performance of the proposed method is assessed through a simulation study. We apply our methods to longitudinal bi-weekly measures of a primary urological urinary symptoms score from a UCPPS longitudinal cohort study, and identify four subgroups ranging from moderate decline, mild decline, stable and mild increasing. The resulting clusters are also associated with the one-year changes in several clinically important outcomes, and are also related to several clinically relevant baseline predictors, such as sleep disturbance score, physical quality of life and painful urgency.
通过将患有泌尿系统慢性盆腔疼痛综合征（UCPPS）的患者聚类为同质亚组，并将这些亚组与基线协变量和其他临床结果相关联，我们提供了研究发病机制中不同潜在因素的机会，这也可能指导我们选择合适的治疗靶点。受具有广泛个体异质性和轨迹差异变异性的纵向泌尿系统症状数据的启发，我们提出了一种功能聚类程序，其中每个亚组由功能混合效应模型建模，并使用后验概率将每个受试者迭代分类到不同的亚组中。该分类同时考虑了组平均轨迹和个体间变异性。我们开发了一个等效的状态空间模型以进行高效计算。我们还提出了一种基于交叉验证的库尔贝克-莱布勒信息准则来选择亚组的最佳数量。通过模拟研究评估了所提出方法的性能。我们将我们的方法应用于来自UCPPS纵向队列研究的原发性泌尿系统症状评分的纵向双周测量，并识别出四个亚组，分别为中度下降、轻度下降、稳定和轻度上升。所得聚类还与几个临床重要结果的一年变化相关，并且还与几个临床相关的基线预测因素相关，如睡眠障碍评分、身体生活质量和疼痛性尿急。
