A single randomized controlled trial (RCT) rarely includes all alternative interventions of interest for a given target patient population and, as such, does not provide all the information needed to select the best treatment alternative. Frequently, the relevant evidence base consist of multiple RCTs, each of which compares a subset of the interventions of interest. If the evidence base can be represented by a network of RCTs, a network meta-analysis (NMA) can estimate relative treatment effects between all the competing interventions. NMA is a commonly used method in the context of health technology assessment of new interventions. Methods for NMA have been developing rapidly since its introduction about 15 years ago and include specific approaches to use both study-level data and patient-level data, methods for time-to-event outcomes, and methods to incorporate observational evidence. In this presentation, an overview of key concepts, assumptions and different NMA methods will be presented.
Speaker: Jeroen Jansen, PhD, Associate Professor, Department of Clinical Pharmacy, UCSF
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