Methods for the inclusion of real world evidence in network meta-analysis

by   David Jenkins, et al.

Background: Network Meta-Analysis (NMA) is a key component of submissions to reimbursement agencies world-wide, especially when there is limited direct head-to-head evidence for multiple technologies from randomised control trials (RCTs). Almost all NMAs include only data from RCTs. However, real-world evidence (RWE) is also becoming widely recognised as a source of clinical data. In this paper, we investigate methods for the inclusion of RWE and its impact on the level of uncertainty around the effectiveness estimates. Methods: We investigated the use of a range of methods for inclusion of RWE in evidence synthesis by applying them to an illustrative example in relapsing remitting multiple sclerosis (RRMS). We carried out a literature search to identify RCTs and RWE evaluating treatments in RRMS. To assess the impact of inclusion of RWE on the effectiveness estimates, we used Bayesian hierarchical and power prior models. We investigated the effect of the inclusion of RWE by varying the degree of down weighting of this part of evidence by the use of a power prior. Results: Whilst the inclusion of the RWE led to an increase in the level of uncertainty surrounding effect estimates for this example, this depended on the method of inclusion adopted for the RWE. The hierarchical models were effective in allowing for heterogeneity between study designs but this also further increased the level of uncertainty. Conclusion: The power prior method for the inclusion of RWE in NMAs indicates that the degree to which RWE is taken into account can have a significant impact on the overall level of uncertainty. The hierarchical modelling approach further allowed for accommodating differences between study types. Further work investigating both empirical evidence for biases associated with individual RWE studies and methods of elicitation from experts on the extent of such biases is warranted.


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