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

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    • Source:
      Publisher: Wiley Blackwell Country of Publication: England NLM ID: 101543738 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1759-2887 (Electronic) Linking ISSN: 17592879 NLM ISO Abbreviation: Res Synth Methods Subsets: MEDLINE
    • Publication Information:
      Publication: : Chichester : Wiley Blackwell
      Original Publication: Malden, MA : John Wiley & Sons, 2010-
    • Subject Terms:
    • Abstract:
      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 the 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.
      (© 2023 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)
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    • Grant Information:
      The HTx project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 825162. This dissemination reflects only the author's view and the Commission is not responsible for any use that may be made of the information it contains.
    • Contributed Indexing:
      Keywords: observational studies; randomized controlled trials; real-world evidence; risk of bias
    • Accession Number:
      0 (Antidepressive Agents)
    • Publication Date:
      Date Created: 20230110 Date Completed: 20230314 Latest Revision: 20230314
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