A Bayesian Model to Analyze the Association of Rheumatoid Arthritis With Risk Factors and Their Interactions.

ABSTRACT: Rheumatoid arthritis (RA) is a chronic autoimmune disorder that commonly manifests as destructive joint inflammation but also affects multiple other organ systems. The pathogenesis of RA is complex where a variety of factors including comorbidities, demographic, and socioeconomic variables are known to associate with RA and influence the progress of the disease. In this work, we used a Bayesian logistic regression model to quantitatively assess how these factors influence the risk of RA, individually and through their interactions. Using cross-sectional data from the National Health and Nutrition Examination Survey (NHANES), a set of 11 well-known RA risk factors such as age, gender, ethnicity, body mass index (BMI), and depression were selected to predict RA. We considered up to third-ord

SUBMITTER: Lufkin L 

PROVIDER: S-EPMC8415718 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature