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Wensheng Guo, PhD

Jialin Yi, Grad Student

Clinical Questions:

1) Can patients with UCPPS be classified reliably into K=3 trajectories of longitudinal functional symptom patterns, such as

1) improve; 2) stable; 3) worse, separately for multiple symptom patterns?

2) Are UCPPS patients likely to be classified into the same longitudinal pattern category for separate outcomes?

Functional Clusters: Longitudinal Symptom Profiles

K=1 Functional Cluster for Pelvic Pain Severity Change Scores

K=1 Functional Cluster for Urinary Severity Change Scores

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Functional Clusters of Longitudinal Symptom PatternsDynamic Functional Time Series Clustering (FTSC) Algorithmo produce initial classifications into K-

clusters based on K-means clustering

using all time points;

o fit functional mixed effects model as

flexible cubic spline to establish the

mean profiles for the K-subgroups;

o incorporate variability over time within

K-groups as flexible cubic spline curve

o iteratively re-classify each subject into

one of the K-subgroups based on the

posterior probability, and update the

profiles of the subgroups until

convergence.

Guo, Yi and Landis (2018). K-class functional

mixed effects clustering with application to

longitudinal urological chronic pelvic pain

syndrome symptom data. To be submitted to

JASA Applications.MATLAB code and documentation for FTSC procedure posted to GitHub at https://github.com/jialinyi94/FTSC

K=1 Functional Cluster for Pelvic Pain Severity Change Scores

K=1 Functional Cluster for Urinary Severity Change Scores

K=3 Functional Clusters for Pelvic Pain Severity Change Scores

K=3 Functional Clusters forUrinary Severity Change Scores