Multilevel network meta-regression for time-to-event outcomes in educational symposium. Indirect treatment comparison methodology matters:unlocking the essentials for robust analysis

Indirect treatment comparisons (ITCs) are frequently required in health technology assessments due to the lack of head-to-head trials for interventions of interest. Methodological guidance for ITCs, such as the European Union Member State Coordination Group's guidelines in supporting Joint Clinical Assessment on quantitative evidence synthesis and NICE's Technical Support Documents on evidence synthesis and population-adjusted indirect comparisons, provide detailed advice on applying various ITC approaches in practice.<br/>Network meta-analysis is the most widely used ITC approach, under the assumption that any variables that modify treatment effects are balanced across the included study populations. Recent population-adjusted ITC methods aim to relax this assumption, adjusting for differences between populations using individual participant data from one or more studies. Despite its limitations – such as requiring and being sensitive to population overlap, being restricted to estimates in aggregate study populations, and being limited to two-study indirect comparisons – matching-adjusted indirect comparison (MAIC) remains the most commonly used population-adjusted ITC approach. However, poorly conducted ITCs can have significant consequences, potentially leading to a potential cost-effective intervention not being reimbursed.<br/>This session will explore innovations in ITC methodologies and introduce alternative approaches to MAIC, including simulated treatment comparison (STC) and multilevel network meta-regression (ML-NMR), with a special focus on applications to time-to-event outcomes. Case studies will be presented to highlight the limitations and pitfalls of MAIC, while demonstrating how alternative methods can address these limitations.<br/>Attendees will gain a comprehensive understanding of the diverse range of ITC approaches and acquire practical knowledge of the key principles needed for performing robust ITC analyses.