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

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Jenkins, David A., Hussein, Humaira, Martina, Reynaldo, Dequen-O’Byrne, Pascale, Abrams, Keith R. and Bujkiewicz, Sylwia (2021) Methods for the inclusion of real-world evidence in network meta-analysis. BMC Medical Research Methodology, 21 (1). 207. doi:10.1186/s12874-021-01399-3

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Official URL: http://dx.doi.org/10.1186/s12874-021-01399-3

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Abstract

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 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.

Methods

A 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.

Results

Whilst 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.

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. 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.

Item Type: Journal Article
Subjects: R Medicine > R Medicine (General)
Divisions: Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH): Meta-analysis, Evidence-based medicine -- Data processing
Journal or Publication Title: BMC Medical Research Methodology
Publisher: BioMed Central Ltd.
ISSN: 1471-2288
Official Date: 2021
Dates:
DateEvent
2021Published
9 October 2021Available
7 September 2021Accepted
Volume: 21
Number: 1
Article Number: 207
DOI: 10.1186/s12874-021-01399-3
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
MR/R025223/1[MRC] Medical Research Councilhttp://dx.doi.org/10.13039/501100000265
NI‐SI‐0512‐10159[NIHR] National Institute for Health Researchhttp://dx.doi.org/10.13039/501100000272
115546FP7 Healthhttp://dx.doi.org/10.13039/100011272
UNSPECIFIED[NIHR] National Institute for Health Researchhttp://dx.doi.org/10.13039/501100000272
MR/L009854/1[MRC] Medical Research Councilhttp://dx.doi.org/10.13039/501100000265
FP7/2007- 2013Seventh Framework Programmehttp://dx.doi.org/10.13039/100011102

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