A0757
Title: Functional mixed effects clustering with application to longitudinal urologic chronic pelvic pain syndrome symptom data
Authors: Wensheng Guo - University of Pennsylvania (United States) [presenting]
Abstract: 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 the 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. 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.
