Indirect treatment comparisons (ITC) are central to health technology assessment as direct evidence on the relative effectiveness of treatments is rarely available. Network meta-analysis is the preferred approach for ITC but may be inadequate in the presence of imbalances in effect modifiers in the network. A growing set of methods are available for population adjusted indirect comparison (PAIC) and are being used increasingly to adjust for this bias. This session takes a close look at effect modification in the context of ITCs to define what it is and uses simple examples to illustrate different patterns and their impact on choice of analytic methods.

Learning Objectives:

Understand what effect modification means in the context of ITCs
Recognize the implications of different patterns of effect modification in the network
Determine if and when population adjustment is appropriate

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