📊 Introduction

This project modeled longitudinal changes in biomedical measures to uncover individual and group-level trends. It combined hierarchical statistics with Bayesian inference to capture complex variability in patient development.

🎯 Objectives

• Model non-linear relationships between growth and clinical variables
• Quantify uncertainty and subject-level variability
• Compare Bayesian vs. frequentist approaches
• Provide visual summaries of credible intervals and trends

⚙️ Methods

• Implemented in R using brms, lme4, and tidybayes
• Posterior predictive checks and model diagnostics
• Visual communication via ggplot2 and posterior
• Reproducible scripts for parameter estimation and visualization

💡 Outcome

Produced robust, interpretable models revealing developmental trajectories and variance across clinical subgroups. This work emphasized statistical rigor and transparency in longitudinal research.