Epidemiologists often deal with complex datasets where several factors may simultaneously influence health outcomes. Confounding occurs when the effect of one variable is mixed with the effect of another. Multivariable regression helps control for these confounders, providing a clearer picture of the true relationship between variables. It is especially useful for:
1. Adjusting for confounders. 2. Assessing the independent effect of each variable. 3. Improving the accuracy of predictive models.
