Simulation for fire 1 and 2

This repository contains MATLAB code and simulation outputs for the Monte Carlo sensitivity analysis presented in the article:<br><b>"Can Unmeasured Confounding Explain COVID-19 Vaccine Effectiveness? A Quantitative Assessment Using Cornfield Bounds and Simulation-Based Sensitivity Analysis"</b> by Tommaso Costa.The simulations estimate the proportion of hypothetical confounders (defined by their associations with both exposure and outcome) that would be strong enough to fully explain observed vaccine effectiveness estimates from real-world studies (e.g., risk ratios of 0.08 and 0.25).<br>The code implements log-uniform priors for RR_CE (confounder-exposure) and RR_CD (confounder-disease) and computes the product RR_CE × RR_CD to assess whether the Cornfield inequality is satisfied.This material supports the figures and quantitative results presented in the article, particularly Figures 1 and 2.