PURPOSE: Indirect comparisons and network meta-analysis (NMA) estimate relative treatment effects between competing health technologies, however these methods may lack validity if there are differences in patient populations between included studies. Furthermore, in Health Technology Assessment (HTA) the estimates need to be relevant to the target population for the decision. This workshop will provide an overview of methods for indirect comparisons and NMA that aim to account for population differences. Advantages and disadvantages of the different methods and implications for future HTA submissions will be discussed.

DESCRIPTION: This workshop will begin with an overview of different approaches to population adjustment for indirect comparisons and NMA. If individual patient data (IPD) are available for all studies then IPD-level network meta-regression is the gold standard approach. However, in HTA IPD are typically only available for one or a subset of studies. Available methods in this case include matching-adjusted indirect comparison (MAIC), simulated treatment comparison (STC), network meta-regression, and the recently developed multilevel network meta-regression (ML-NMR). The methods will be compared as to their: validity; flexibility to synthesize networks of any size and availability of IPD; and ability to produce estimates in a given target population of interest. Their performance in simulation studies will be presented. Recommendations on when to use these methods for future HTA submissions will then be discussed based on hypothetical yet representative case-studies characterized by different evidence scenarios. Dr. Jansen will introduce current methods for population adjustment in HTA. Dr. Phillippo will then describe ML-NMR and compare the methods. Finally, Prof Welton will discuss recommendations for HTA. Presentations will last 40 minutes, followed by an interactive Q&A session, providing the audience an extended opportunity to discuss the issues raised with the panel. This workshop will benefit those involved in preparing or critiquing HTA submissions that utilize population adjustment methods.