When there is more than one treatment option available in a therapeutic area, decision makers – from regulators and payers to providers and those developing care guidelines – all require evidence of a product’s performance in comparison to the alternative(s).

Direct head-to-head comparisons of all relevant comparators serve this purpose, but may not always be possible or necessary within randomised controlled trials (RCTs). In certain situations, it is possible to synthesise existing evidence from multiple studies to calculate a pooled treatment effect and thus demonstrate the comparative performance of a novel treatment against established interventions.

However, even when there are no direct, head-to-head comparisons available, it is nonetheless possible to estimate effects through indirect comparisons using specific methods of evidence synthesis. In this whitepaper we explain the concepts behind the various advanced statistical methods used to calculate a treatment effect from data that has been pooled from across more than one RCT.