maic-methodologylisted

# MAIC Methodology Comprehensive methodological guidance for conducting rigorous Matching-Adjusted Indirect Comparisons following NICE DSU TSD 18. ## When to Use This Skill - Deciding whether to use MAIC vs other ITC methods - Selecting covariates for matching - Interpreting weight diagnostics and ESS - Choosing between anchored and unanchored MAIC - Reviewing MAIC code or results ## Fundamental Assumptions ### Key Assumption: Conditional Constancy of Relative Effects **For Anchored MAIC**: - The relative treatment effect (vs common comparator) is the same across populations AFTER adjusting for effect modifiers - This is untestable - relies on clinical judgment - Requires all effect modifiers to be included in matching ### No Unmeasured Effect Modifiers ``` Critical: MAIC assumes that adjusting for measured covariates removes all population differences that modify treatment effects. If there are unmeasured effect modifiers: ├── Anchored MAIC: Biased indirect comparison └── Unanchored MAIC: Even more biased There is NO WAY to test this assumption with available data. ``` ### Unanchored MAIC: Additional Assumptions - All prognostic factors (not just effect modifiers) must be adjusted - Absolute treatment effects are transportable across populations - Much stronger, often implausible assumptions - Should be avoided if anchored is possible ## When to Use MAIC ### MAIC is Appropriate When: 1. IPD available for one trial (index trial) 2. Only AgD available for compara