Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study.

ABSTRACT: Standard network meta-analysis and indirect comparisons combine aggregate data from multiple studies on treatments of interest, assuming that any factors that interact with treatment effects (effect modifiers) are balanced across populations. Population adjustment methods such as multilevel network meta-regression (ML-NMR), matching-adjusted indirect comparison (MAIC), and simulated treatment comparison (STC) relax this assumption using individual patient data from one or more studies, and are becoming increasingly prevalent in health technology appraisals and the applied literature. Motivated by an applied example and two recent reviews of applications, we undertook an extensive simulation study to assess the performance of these methods in a range of scenarios under various failures of a

SUBMITTER: Phillippo DM 

PROVIDER: S-EPMC8690023 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature