Methods: EVSI measures the expected net health gains from conducting a new research study given a proposed study design. EVSI relies on a synthesis of the current evidence available on treatment efficacy, and a cost-effectiveness model. Network Meta-Analysis (NMA) pools together evidence on relative efficacy of multiple competing health technologies that have been compared in Randomised Controlled Trials that form a connected network of comparisons. The results obtained from NMA provide a coherent basis on which to make comparisons across the entire set of treatments, and NMA is now commonly used to inform decision models to identify the most cost-effective treatment.
We describe methods to evaluate EVSI when the efficacy outcome is binary and the net benefit function is linear on the absolute probability scale. We distinguish between absolute effects (used in the decision model) and relative effects (which the RCT provides information on). The methods allow for heterogeneity in the existing NMA evidence, which forms a hierarchical prior for the result from the new study. We view this hierarchical prior structure as data so that we can obtain a posterior, given new data, in closed form. We use a Taylor series approximation to obtain the updated expectation of the net benefit given new data, without needing an inner simulation step.
Results: We illustrate the approach using as an example a network meta-analysis and cost-effectiveness analysis of 6 competing treatments for bipolar disorders, to identify the optimal number of arms and sample size per arm to include in a new study to inform this decision.
Conclusions: EVSI can be a valuable tool to assist in the prioritisation and optimal design of new research studies when there are multiple competing technologies.