Applying binary mixed model to predict knee osteoarthritis pain.

ABSTRACT: Data used to understand knee osteoarthritis (KOA) often involves knee-level, rather than person-level information. Failure to account for the correlation between joints within a person may lead to inaccurate inferences. The aim of this study was to develop a flexible, data-driven framework for predicting knee pain outcomes, incorporating the advantages of both random forest (RF) and mixed effects models for correlated data. Specifically, we utilized data from the baseline visit of the Osteoarthritis Initiative (OAI) and applied the Binary Mixed Models (BiMM) algorithm to predict two binary dependent variables. 1) presence of knee pain, stiffness or aching in the past 12 months and 2) presence of knee pain indicated by a KOOS pain score > 85. This novel approach was compared to standard ran

SUBMITTER: El-Zaatari H 

PROVIDER: S-EPMC12262897 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature