I am a clinical biostatistician and a medical student at Jordan University of Science and Technology, and I run independent biostatistics for clinical research teams, taking projects from raw data to a submission-ready manuscript.

My day-to-day methods are the ones these courses teach: PRISMA-compliant systematic review and meta-analysis, Bayesian and frequentist network meta-analysis, diagnostic test accuracy and dose-response models, survival modelling with competing risks, propensity-matched pooling, rare-event methods, and registry analysis at population scale with SEER and CDC WONDER. I have around eighteen publications, including first-author work in Blood, the Journal of the American College of Cardiology and the Journal of Clinical Oncology, and co-authored analyses across vascular surgery, neurology and cardiothoracic surgery.

I teach because I struggled early on. There was very little guidance and almost nothing up to date that showed the actual process of producing a paper that survives peer review. That gap is what MetaTree was built to close, from introductory terminology through to advanced workflows that take the arrival of AI in research seriously.

My courses are built the way I work. Every number on screen is one you can reproduce yourself in R, every method is shown on a real dataset, and nothing is claimed that the data cannot support.