Biostatistician and data scientist focused on statistical modeling, reproducible research, and applied health analytics.
I’m currently working on reproducible statistical analysis pipelines for biomedical research, including mixed-effects modeling, simulation-based power analysis, and interpretable modeling of clinical and perceptual outcomes.
- Generalized Linear Models and Mixed Effects Models
- Statistical Modeling for Biomedical Data
- Causal Inference and Observational Study Design
- Simulation-Based Power Analysis
- Reproducible Data Science Workflows
🌱 I’m currently learning more about scalable statistical computing and best practices for production-ready data science workflows. Learning French too! 🇫🇷
👯 I’m looking to collaborate on applied statistical modeling projects in healthcare, clinical research, and real-world evidence.
R • Python • SQL • Git • Bash
tidyverse • lme4 • brms • scikit-learn • JAX
Statistical modeling of postoperative breast geometry and its association with aesthetic and naturalness ratings using mixed-effects ordinal and logistic regression models.
Development of simulation frameworks to evaluate statistical power under varying sample sizes, clustering structures, and missing data patterns.
Implementation of reproducible workflows for bulk RNA-seq data quality assessment and downstream analysis.
Most projects in this repository use:
renvfor environment reproducibility- Quarto / R Markdown for reports
- Git for version control
LinkedIn: [https://www.linkedin.com/in/casandra-serafin/]