Functional mixed effects clustering with application to longitudinal urologic chronic pelvic pain syndrome symptom data

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.

本研究将泌尿慢性盆腔疼痛综合征（urologic chronic pelvic pain syndromes, UCPPS）患者聚类为同质亚组，并将这些亚组与基线协变量及其他临床结局相关联，为探究发病机制的不同潜在要素提供了契机，同时也可为筛选合适的治疗靶点提供指导。针对存在显著个体异质性且轨迹变异性存在差异的纵向泌尿症状随访数据，我们提出了一种函数型聚类方法：各亚组均通过函数型混合效应模型进行建模，并利用后验概率对每个受试者进行迭代分类，将其归入不同亚组。该分类过程同时兼顾了组平均轨迹与个体间变异。为实现高效计算，我们推导了等价的状态空间模型；此外，我们还提出了基于交叉验证的库勒贝克-莱布勒信息准则（Kullback-Leibler information criterion），以选择最优的亚组数目。通过模拟研究对所提方法的性能进行了评估。我们将该方法应用于一项UCPPS纵向队列研究中每两周一次的原发性泌尿尿路症状评分纵向测量数据，识别出四类亚组，分别为中度降低组、轻度降低组、稳定组与轻度升高组。所得聚类结果还与多项重要临床结局的一年变化量相关联，同时也与睡眠障碍评分、躯体生活质量及痛性尿急等多项临床相关基线预测因素存在关联。