Synthesizing cross-design evidence and cross-format data using network meta-regression.

Hamza, Tasnim; Chalkou, Konstantina; Pellegrini, Fabio; Kuhle, Jens; Benkert, Pascal; Lorscheider, Johannes; Zecca, Chiara; Iglesias-Urrutia, Cynthia P; Manca, Andrea; Furukawa, Toshi A; Cipriani, Andrea; Salanti, Georgia (2023). Synthesizing cross-design evidence and cross-format data using network meta-regression. Research Synthesis Methods, 14(2), pp. 283-300. Wiley 10.1002/jrsm.1619

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In network meta-analysis (NMA), we synthesize all relevant evidence about health outcomes with competing treatments. The evidence may come from randomized clinical trials (RCT) or non-randomized studies (NRS) as individual participant data (IPD) or as aggregate data (AD). We present a suite of Bayesian NMA and network meta-regression (NMR) models allowing for cross-design and cross-format synthesis. The models integrate a three-level hierarchical model for synthesizing IPD and AD into four approaches. The four approaches account for differences in the design and risk of bias (RoB) in the RCT and NRS evidence. These four approaches variously ignoring differences in RoB, using NRS to construct penalized treatment effect priors and bias-adjustment models that control the contribution of information from high RoB studies in two different ways. We illustrate the methods in a network of three pharmacological interventions and placebo for patients with relapsing-remitting multiple sclerosis. The estimated relative treatment effects do not change much when we accounted for differences in design and RoB. Conducting network meta-regression showed that intervention efficacy decreases with increasing participant age. We also re-analysed a network of 431 RCT comparing 21 antidepressants, and we did not observe material changes in intervention efficacy when adjusting for studies' high RoB. We re-analysed both case studies accounting for different study RoB. In summary, the described suite of NMA/NMR models enables inclusion of all relevant evidence while incorporating information on the within-study bias in both observational and experimental data and enabling estimation of individualized treatment effects through the inclusion of participant characteristics. This article is protected by copyright. All rights reserved.

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