Bridging disconnected networks of first and second lines of biologic therapies in rheumatoid arthritis with registry data: Bayesian evidence synthesis with target trial emulation

TL;DR

This paper introduces a Bayesian evidence synthesis method combining real-world registry data with RCTs to improve estimates of biologic therapy effectiveness across treatment lines in rheumatoid arthritis.

Contribution

It develops a novel bivariate network meta-analysis approach that integrates target trial emulation with registry data to enhance treatment effect estimates for both first- and second-line therapies.

Findings

Reduced uncertainty in second-line treatment effectiveness estimates.

Enabled predictions for treatment effects not directly evaluated in RCTs.

Demonstrated potential for bridging trial networks across different patient populations.

Abstract

Objective: We aim to utilise real world data in evidence synthesis to optimise an evidence base for the effectiveness of biologic therapies in rheumatoid arthritis in order 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 (BSRBR-RA) to supplement RCT 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 and second-line treatments. Results: Summary data were obtained from 21 trials of biologic therapies including 2 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 RCTs. 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.