Abstract
Population-adjusted indirect comparisons (PAICs) support the comparative efficacy of treatments in health technology assessments (HTAs). Typically, researchers have access to the individual patient data (IPD) from one trial and the aggregate data (AgD) for comparators. In a PAIC, IPD data is adjusted such that it reflects the AgD data, providing a relatively unbiased relative treatment effect (RTE) estimated in AgD population. However, HTA discussions focus on the IPD population, not the AgD population. Assuming shared-effect modification (SEM), the RTE can be projected into any target population, but violation of SEM can lead to misinterpretation of an indirect treatment comparison (ITC) result and is demonstrated here.
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