Celestin M.

I am a PhD-trained biostatistician and AI/machine learning consultant helping healthcare, life-sciences, and research teams turn complex data into clear, defensible, decision-ready evidence. I support projects from study design and statistical analysis planning through data preparation, modeling, interpretation, and publication-ready reporting. My work combines rigorous biostatistical methods with practical machine learning approaches to answer clinically and scientifically meaningful questions. My areas of expertise include: - Statistical analysis plans, study design, and sample-size/power calculations - Clinical, observational, real-world, and longitudinal data analysis - Survival analysis, Cox regression, competing risks, recurrent events, and mixed-effects models - Regression modeling for continuous, binary, count, ordinal, and time-to-event outcomes - Predictive modeling, risk stratification, model validation, calibration, and performance evaluation - Machine learning workflows for healthcare and life-science data - Causal inference, propensity-score methods, missing-data methods, and sensitivity analyses - Reproducible analytics, data visualization, statistical tables, and manuscript Methods/Results sections I work primarily in R, SAS, Python, SQL, and Excel, and I am comfortable working with complex clinical databases, EHR-derived data, registries, survey data, and research datasets. Clients value my ability to translate technical results into clear recommendations, produce high-quality deliverables, and provide reliable support through manuscript revision, peer review, and stakeholder discussions. Whether you need a focused analysis or an ongoing statistical partner, I bring methodological rigor, responsiveness, and a practical focus on your project goals.