Functional Mixed Effects Clustering with Application to Longitudinal Urologic Chronic Pelvic Pain Syndrome Symptom Data

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 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. Supplementary materials for this article are available online.

Keywords:

• Functional clustering
• Kullback–Leibler information criterion
• Smoothing spline
• State space model

Supplementary Materials

The Matlab codes for both data analysis and simulation are provided in the supplement. The dataset used in the application is also provided.

Acknowledgments

We thank the editor, the associate editor, and three reviewers for their insightful comments that substantially improve this article.

Additional information

Funding

This research was supported by NIDDK, National Institutes of Health cooperative agreement DK82370, DK82342, DK82315, DK82344, DK82325, DK82345, DK82333, and DK82316, R01-GM104470, and R01-DK117208.