Enhanced Simulated Treatment Comparison Through Direct Marginalization

Music City Center 

Population-adjusted indirect comparisons (PAICs) are essential in health technology assessments (HTAs) for evaluating treatment effectiveness when direct head-to-head randomized controlled trials are unavailable. Simulated treatment comparison (STC) is a key PAIC method to adjust for baseline differences between trial populations and enable valid comparisons. However, traditional STC methods regress baseline covariates at the individual level in the index trial but estimate conditional treatment effects at mean covariate values in the comparator trial. This mismatch introduces aggregation bias in marginal treatment effect estimates. This paper introduces STC with Direct Marginalization (STC-DM), a novel extension to improve marginal treatment effect estimation. By directly accounting for baseline covariate heterogeneity, STC-DM mitigates aggregation bias and enhances population comparability. Simulation studies demonstrate its superior accuracy and robustness over standard STC methods. The presentation concludes with a discussion on STC-DM's implications and applications, highlighting its potential to enhance the validity and reliability of comparative effectiveness evaluations.

Indirect treatment comparisons

Marginal treatment effect

Population adjustment

Simulated treatment comparison 

Biopharmaceutical Section