Applying binary mixed model to predict knee osteoarthritis pain.
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 predi...
Saved in:
| Main Authors: | Helal El-Zaatari, Liubov Arbeeva, Amanda E Nelson |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0325678 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Applying binary mixed model to predict knee osteoarthritis pain
by: Helal El-Zaatari, et al.
Published: (2025-01-01) -
Identifying trajectories of joint space width loss among previously injured knees: Data from the Osteoarthritis Initiative.
by: Mary Catherine C Minnig, et al.
Published: (2025-01-01) -
Factors associated with knee pain at early stages of osteoarthritis
by: A. R. Khalmetova, et al.
Published: (2025-06-01) -
Risk factors for neuropathic pain in patients with knee osteoarthritis
by: ZHANG Yijing, ZHOU Xinjie, WANG Lei, FAN Kaiyong, LI Tieshan
Published: (2025-06-01) -
Applying evidence-based cross-disciplinary concepts helps to explain the heterogeneity in pain, function, and biological measures in individuals with knee pain with/at risk of osteoarthritis
by: Angela M. Mickle, et al.
Published: (2025-02-01)