Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume 117, Issue 540, Pages 1631-1641Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/01621459.2022.2066536
Keywords
Functional clustering; Kullback-Leibler information criterion; Smoothing spline; State space model
Categories
Funding
- NIDDK, National Institutes of Health [DK82370, DK82342, DK82315, DK82344, DK82325, DK82345, DK82333, DK82316, R01-GM104470, R01-DK117208]
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By clustering UCPPS patients into homogeneous subgroups and utilizing longitudinal data modeling, we are able to identify different trajectories of the disease and associate them with clinical outcomes and baseline predictors. The proposed method shows promising performance in simulation studies and identifies four distinct subgroups of UCPPS patients, each with varying disease progression patterns.
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.
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