Development of predictive biomarker spectrum for neuropsychiatric systemic lupus erythematosus (NPSLE)

A collaboration with Graduate School of Biomedical Sciences, Nagasaki University, Japan

• Topics: Bayesian modelling, predictive modelling, health science, biomarker evaluation
• Contributions: Data analysis, Bayesian statistical modelling, data visualization, writing and editing article manuscript (Bayesian-related part)
• Tools: R, SAS, RMarkdown, LaTeX, Git, GNU Make

→ GitHub repository
→ Publication

This collaborative project aims to identify a potential spectrum of biomarkers for predicting NPSLE, offering crucial clinical insights before the onset of neuropsychiatric symptoms. Using a novel Bayesian model, the project determined the cutoff concentration of anti-suprabasin antibodies and associated predictive values (PPV, NPV). The findings from this project could assist in clinical trials, aid in making decisions about patient care, guide treatment choices, and enable cost-effective interventions.