Bayesian Meta-Regression for Clinical Side-Effects

I have gathered a collection of randomized clinical-trial results in a single Excel workbook and want to quantify treatment-related side-effects with a fully Bayesian approach. The task is to import the Excel data, run a random-effects Bayesian meta-analysis, and extend it with meta-regressions that explore how study-level covariates influence the incidence of adverse events. I work most comfortably in R, so please build the workflow there—packages such as brms, rstanarm, bayesmeta, or similar are fine as long as the code is transparent and reproducible. If you prefer to prototype in Stata that is acceptable, but the final deliverable must run end-to-end in R. Key points to cover • Clean and prepare the Excel dataset for analysis • Specify appropriate priors and justify them briefly in code comments • Fit the base model (overall pooled estimate) and the meta-regression models • Provide convergence diagnostics, posterior summaries, forest and funnel plots, and a short written interpretation of the findings • Supply the commented R script, the rendered report (R Markdown/HTML or PDF), and any auxiliary files needed to reproduce the results I’m flexible on the timeline; accuracy and clarity matter more than speed. Feel free to suggest sensitivity checks or alternative model specifications if they improve robustness.