Abstract
BackgroundNetwork 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 controlled trials (RCTs). Many NMAs include only data from RCTs. However, real-world evidence (RWE) is also becoming widely recognised as a valuable source of clinical data. This study aims to investigate methods for the inclusion of RWE in NMA and its impact on the level of uncertainty around the effectiveness estimates, with particular interest in effectiveness of fingolimod.MethodsA range of methods for inclusion of RWE in evidence synthesis were investigated by applying them to an illustrative example in relapsing remitting multiple sclerosis (RRMS). A literature search to identify RCTs and RWE evaluating treatments in RRMS was conducted. To assess the impact of inclusion of RWE on the effectiveness estimates, Bayesian hierarchical and adapted power prior models were applied. The effect of the inclusion of RWE was investigated by varying the degree of down weighting of this part of evidence by the use of a power prior.ResultsWhilst the inclusion of the RWE led to an increase in the level of uncertainty surrounding effect estimates in this example, this depended on the method of inclusion adopted for the RWE. ‘Power prior’ NMA model resulted in stable effect estimates for fingolimod yet increasing the width of the credible intervals with increasing weight given to RWE data. The hierarchical NMA models were effective in allowing for heterogeneity between study designs, however, this also increased the level of uncertainty.ConclusionThe ‘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. Consequently, 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.
Highlights
Network Meta-Analysis (NMA) is a key component of submissions to reimbursement agencies worldwide, especially when there is limited direct head-to-head evidence for multiple technologies from randomised controlled trials (RCTs)
The numbers along the lines represent the number of studies for each comparison in either the RCTs or real-world evidence (RWE)
While the ‘power transform prior’ network meta-analysis (NMA) as well as hierarchical NMA models had little impact on annualised relapse rate ratio (ARRR) effect estimates, the degree of inclusion of RWE in the NMAs impacted the level of uncertainty around these effect estimates, likely as a result of increased between-study heterogeneity
Summary
Network Meta-Analysis (NMA) is a key component of submissions to reimbursement agencies worldwide, especially when there is limited direct head-to-head evidence for multiple technologies from randomised controlled trials (RCTs). There has been a growing interest in the use of real-world evidence (RWE) from observational studies in health-care evaluation [1, 2] The potential advantage of RWE, for the purpose of health technology assessment (HTA) decision-making, is that it can be a substantial source of evidence increasing the available evidence base as well as better representing “real-life” clinical practice. To this extent, RWE can be used to bridge a gap between efficacy and effectiveness to ensure that the evaluation process reflects what is expected in clinical practice in terms of effectiveness of new health technologies. Recent methodological developments focus on appropriate methods of using such data
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