stc-methodologylisted

# STC Methodology Comprehensive methodological guidance for conducting rigorous Simulated Treatment Comparisons following NICE DSU TSD 18. ## When to Use This Skill - Deciding between STC and MAIC - Selecting effect modifiers for an STC model - Implementing covariate centering on an aggregate target population - Reviewing STC code or results - Planning Bayesian or frequentist sensitivity analyses ## Fundamental Concept ### Outcome Regression vs Propensity Weighting **STC approach** - Fit an outcome regression model in the IPD study. - Include treatment and treatment-covariate interactions for relevant effect modifiers. - Center covariates on the external aggregate population. - Interpret the treatment coefficient as the adjusted effect in that external population. **MAIC approach** - Reweight IPD to match external aggregate covariate summaries. - Estimate the weighted treatment effect in the target population. - Use weight diagnostics and effective sample size as core feasibility checks. ### Key Equation for a Binary Anchored STC ```text logit{P(Y = 1)} = beta_0 + beta_trt * Treatment + beta_X * X_centered + beta_trt_X * Treatment * X_centered X_centered = X - X_external ``` With centered covariates, `beta_trt` estimates the treatment effect in the external population, because `X_centered = 0` corresponds to the aggregate target values. ## Assumptions ### Conditional Constancy of Relative Effects - Anchored STC assumes relative ef