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

Tasnim Hamza1,2, Konstantina Chalkou1,2, Fabio Pellegrini3

  • 1Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.

Research Synthesis Methods
|January 10, 2023
PubMed
Summary

This study introduces Bayesian network meta-analysis (NMA) and network meta-regression (NMR) models to synthesize diverse evidence, including randomized clinical trials and non-randomized studies, while accounting for risk of bias. Findings suggest robust treatment effect estimates even with bias adjustments.

Keywords:
observational studiesrandomized controlled trialsreal-world evidencerisk of bias

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Area of Science:

  • Biostatistics
  • Health Economics
  • Evidence Synthesis

Background:

  • Network meta-analysis (NMA) synthesizes evidence from multiple studies for comparative effectiveness.
  • Evidence often includes randomized clinical trials (RCTs) and non-randomized studies (NRS), presenting challenges in data format (individual participant data [IPD] vs. aggregate data [AD]) and risk of bias (RoB).

Purpose of the Study:

  • To present a suite of Bayesian NMA and network meta-regression (NMR) models for cross-design and cross-format evidence synthesis.
  • To develop approaches that account for differences in study design and RoB when integrating RCT and NRS data.
  • To enable the inclusion of all relevant evidence, including bias information, for more comprehensive treatment effect estimation.

Main Methods:

  • Developed a three-level hierarchical model for synthesizing IPD and AD.
  • Proposed four approaches to integrate RCT and NRS evidence, including methods that ignore RoB, use NRS for penalized priors, and implement bias-adjustment models.
  • Applied the models to a network of treatments for relapsing-remitting multiple sclerosis and a large network of antidepressants.

Main Results:

  • Estimated relative treatment effects remained largely consistent after accounting for design and RoB differences in the multiple sclerosis network.
  • Network meta-regression revealed that intervention efficacy decreased with increasing participant age.
  • Adjusting for RoB in a large antidepressant network did not materially alter intervention efficacy estimates.

Conclusions:

  • The suite of NMA/NMR models effectively synthesizes diverse evidence types (RCT, NRS, IPD, AD) while incorporating within-study bias information.
  • The methods allow for robust comparative effectiveness assessments and the estimation of individualized treatment effects based on participant characteristics.