Confounding variable control strategies

Control for confounding variables in observational studies. Design-based controls: 1. Randomization: Random assignment eliminates selection bias. 2. Restriction: limit study to homogeneous group (e.g., only males, specific age range). 3. Matching: match cases and controls on potential confounders (age, gender, education). Analysis-based controls: 1. Stratification: analyze results within strata of confounder levels. 2. Multiple regression: include confounders as covariates in regression model. 3. Propensity score matching: calculate probability of exposure, match on propensity scores. 4. Instrumental variables: use natural randomization when available. Assessment: Create directed acyclic graphs (DAGs) to identify confounders vs. mediators vs. colliders. Use causal inference framework to determine which variables to control. Report all controlled variables and rationale for inclusion.