Journal
JOURNAL OF CLINICAL EPIDEMIOLOGY
Volume 150, Issue -, Pages 171-178Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jclinepi.2022.06.011
Keywords
Real world evidence; Bivariate network meta-analysis; Target trial emulation; Treatment lines; Rheumatoid arthritis; Biologic therapies
Funding
- Innovative Medicines Initiative Joint Under- taking
- European Union
- UK Medical Research Council (MRC)
- Methodology Research Panel (MRP)
- MRC MRP
- UK National Institute for Health Research (NIHR) Methods Fellowship
- NIHR Manchester Biomedical Research Centre
- AbbVie
- Amgen [115546]
- Celltrion
- Eli Lilly
- Pfizer [MR/R025223/1]
- Samsung [MR/L009854/1]
- Sandoz [RM-FI-2017-08-027]
- Sanofi
- BSR
- National Institute for Health Research, through the Comprehensive Local Research Networks at participating centers
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This study aims to optimize the evidence base for biologic therapies in rheumatoid arthritis using real-world data, allowing evidence on first-line therapies to inform estimates of effectiveness in second-line treatment. By emulating treatment sequences and conducting a bivariate network meta-analysis, the study provides effectiveness estimates for both first-line and second-line treatments.
Objectives: We aim to use real-world data in evidence synthesis to optimize an evidence base for the effectiveness of biologic therapies in rheumatoid arthritis to allow for evidence on first-line therapies to inform second-line effectiveness estimates. Study Design and Setting: We use data from the British Society for Rheumatology Biologics Register for Rheumatoid Arthritis to supplement randomized controlled trials evidence obtained from the literature, by emulating target trials of treatment sequences to estimate treatment effects in each line of therapy. Treatment effects estimates from the target trials inform a bivariate network meta-analysis (NMA) of first-line and second-line treatments. Results: Summary data were obtained from 21 trials of biologic therapies including two for second-line treatment and results from six emulated target trials of both treatment lines. Bivariate NMA resulted in a decrease in uncertainty around the effectiveness estimates of the second-line therapies, when compared to the results of univariate NMA, and allowed for predictions of treatment effects not evaluated in second-line randomized controlled trials. Conclusion: Bivariate NMA provides effectiveness estimates for all treatments in first and second line, including predicted effects in second line where these estimates did not exist in the data. This novel methodology may have further applications; for example, for bridging networks of trials in children and adults. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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