Bayesian Mixture of Ordinal Regressions for Modeling Pain Score Trajectories

1661 

Contributed Abstract 

Poster 

Youngsoo Baek (1), Rushi Tang (1), Orlando Chen (2), Samuel Berchuck (1)

(1) N/A, N/A, (2) Duke University, N/A

Rushi Tang  
N/A

Orlando Chen  
Duke University

Samuel Berchuck  
N/A

Youngsoo Baek  
N/A

Youngsoo Baek  
N/A

We present a Bayesian mixture model to cluster longitudinal pain score trajectories of breast cancer patients. Pain scores are integer-valued scores, ranging on a scale from 0 to 10, and are routinely reported at medical visits by patients throughout the course of their treatment. We focus on modeling and clustering patients' pain score trajectories from their initial cancer diagnosis throughout their treatment to better understand distinct "risk profiles" over time with the hope of tailored pain treatment interventions. We build a mixture model using Gaussian process regressions of pain scores on times, where each cluster likelihood is parameterized by latent, continuous-time pain trajectories and ordered cut points. We study model sensitivity to the choice of the number of mixtures and stability of the identified clusters through simulation studies and posterior predictive checks. We then fit the model to Duke breast cancer patient data and discuss clinical insights gained from cluster-associated patient demographics.

Mixture model|Trajectory clustering|Bayesian modeling|Pain scores data|Clinical statistics|

Section on Bayesian Statistical Science

Applications in Life Sciences and Medicine