OBJECTIVES: Population-adjusted indirect treatment comparisons (ITCs) as simulated treatment comparison (STC) and matching-adjusted indirect comparison (MAIC) are more and more used to overcome lack of connectivity or imbalances in treatment effect modifiers across trials. This comparison of the two approaches aims at presenting key differences of the two methods and cases where each approach should be preferred.

METHODS: Recommendations from NICE, including the Technical Support Document (TSD) 18 related to population-adjusted ITCs were reviewed and used to create this summarised comparison of STC and MAIC and provide support on the decision of the most appropriate methodology.

RESULTS: While both MAIC and STC methods can be used to overcome heterogeneity or lack of connectivity in ITCs, both approaches differ in terms of assumptions and implementations. This comparison of the two methods considers the objective, principle, underlying assumptions, outcome scale and associated link function, implementation steps and ways to assess validity. These items were used to suggest situations where MAIC and STC may be more appropriate, including in terms of outcomes of interest, overall objectives of the comparison, set of analyses or data availability. While additional steps and assumptions are required for STC when considering time-to-event outcomes, STC can sometimes offer more flexibility around subgroups limited by a lack of reporting compared to MAIC.

CONCLUSIONS: This summarised comparison of MAIC and STC can be used by analysts working on ITCs and facing heterogeneity or lack of connectivity during feasibility assessment phase to get a better understanding of the MAIC and STC and to define the most appropriate approach. This comparison highlights also the advantages of the recent multi-level network meta-regression (ML-NMR) approach, overcoming some MAIC and STC limitations as the focus on pairwise comparisons and on a specific target population.