Methods for information-sharing in network meta-analysis: implications for inference and policy

Limited evidence on relative effectiveness is common in Health Technology Assessment (HTA), often due to sparse evidence on the population of interest or study-design constraints. When evidence directly relating to the policy decision is limited, the evidence base could be extended to incorporate indirectly related evidence. For instance, a sparse evidence base in children could borrow strength from evidence in adults to improve estimation and reduce uncertainty. In HTA, indirect evidence has typically been either disregarded ('splitting'; no information-sharing) or included without considering any differences ('lumping'; full information-sharing). However, sophisticated methods that impose moderate degrees of information-sharing have been proposed. We describe and implement multiple information-sharing methods in a case-study evaluating the effectiveness, cost-effectiveness and value of further research of intravenous immunoglobulin for severe sepsis and septic shock. We also provide metrics to determine the degree of information-sharing. Results indicate that method choice can have significant impact. Across information-sharing models, odds ratio estimates ranged between 0.55 and 0.90 and incremental cost-effectiveness ratios between £16,000-52,000 per quality-adjusted life year gained. The need for a future trial also differed by information-sharing model. Heterogeneity in the indirect evidence should also be carefully considered, as it may significantly impact estimates. We conclude that when indirect evidence is relevant to an assessment of effectiveness, the full range of information-sharing methods should be considered. The final selection should be based on a deliberative process that considers not only the plausibility of the methods' assumptions but also the imposed degree of information-sharing.