Abstract
Population-adjusted ITC, simulated treatment comparison (STC) and matching-adjusted indirect comparison (MAIC), enable comparative effectiveness of therapies for use in health technology assessments in absence of head-to-head trials. While these methods adjust for baseline characteristics differences between-study, the degree of imbalance may impact the outcomes regression model. This simulation evaluated the magnitude of covariate imbalance at different effect modification strengths on population adjusted estimates of treatment efficacy.
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