Network meta-analysis (NMA) is a powerful tool to compare multiple treatments directly and indirectly by combining and contrasting multiple in-dependent clinical trials. Because many NMAs collect only a few eligible randomized controlled trials (RCTs), there is an urgent need to synthesize different sources of information, for example, from both RCTs and single-arm trials. However, single-arm trials and RCTs may have different populations and quality so that assuming they are exchangeable may be inappropriate. This article presents a novel method using a commensurate prior on variance (CPV) to borrow variance (rather than mean) information from single-arm trials in an arm-based (AB) Bayesian NMA. We illustrate the advantages of this CPV method by reanalyzing an NMA of immune checkpoint inhibitors in cancer patients. Comprehensive simulations investigate the impact on statistical inference of including single-arm trials. The simulation results show that the CPV method provides efficient and robust estimation, even when the two sources of information are moderately inconsistent.
Bibliographical noteFunding Information:
Funding. This research was supported in part by the U.S. National Institutes of Health grant R01 LM012982 and the Clinical and Translational Science Award UL1TR002494.
© Institute of Mathematical Statistics, 2021.
- and phrases. Bayesian inference
- commensurate prior
- network meta-analysis
- randomized controlled trial
- single-arm trial