A Beta-Binomial Model for Analyzing Zero- or One-Inflated Pain Trajectories

Music City Center 

Chronic pain is a major public health issue imposing substantial health, emotional, and economic burden on the population. Pain, an inherently subjective experience, is typically measured by patient-reported scores, often on an 11-point scale (0–10). Recent studies assess pain using ecological momentary assessment (EMA), with one or more assessments daily over multiple days, and longitudinally (e.g. pre- and post-intervention). The data often exhibit zero (no pain) or one (maximum pain) inflation. Also, there is substantial within-person variability both within and across days. Statistical modeling of pain trajectories thus present challenges. We propose a beta-binomial (BB) model to estimate potential zero- or one-inflated pain scores over time using a Bayesian approach via Hamiltonian Monte Carlo algorithm implemented in Stan. The model accounts for within-person variability using random effects in both location and dispersion parameters of BB distribution. Simulation study shows our method provides valid posterior inference on all model parameters for sufficiently rich data. The method offers a powerful framework for studying mechanisms underlying patient-reported pain scores.

Chronic Pain

Beta-Binomial

Zero Inflation

Bayesian

Trajectory

EMA 

Biometrics Section