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A multilevel approach to network meta-analysis within a frequentist framework.

Teresa Greco1, Valeria Edefonti2, Giuseppe Biondi-Zoccai3

  • 1Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, Milan, Italy; Laboratorio di Statistica Medica, Biometria ed Epidemiologia "G. A. Maccacaro", Dipartimento di Scienze Cliniche e di Comunità, University of Milan, Milan, Italy.

Contemporary Clinical Trials
|March 26, 2015
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Summary

This study introduces multilevel network meta-analysis, a frequentist approach for synthesizing evidence. It accounts for study dependencies, publication bias, and inconsistency, offering a flexible alternative to traditional methods.

Keywords:
Clinical trialsHierarchical modelsMeta-analysisMultivariate dataNetwork meta-analysisStatistical modeling

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Research Synthesis

Background:

  • Meta-analysis is crucial for summarizing research findings.
  • Existing methods like pairwise and network meta-analysis use multivariate models to handle dependent treatment estimates and study correlations.
  • Meta-analysis can be conceptualized as a form of multilevel analysis due to its hierarchical data structure.

Purpose of the Study:

  • To introduce a novel frequentist approach: multilevel network meta-analysis.
  • To extend meta-analytic capabilities by accounting for publication bias and inconsistency.
  • To propose a flexible, arm-based multilevel modeling strategy for meta-analysis.

Main Methods:

  • A three-level data structure is proposed: arms within studies, studies within designs, and designs within configurations.
  • The approach utilizes an arm-based data structure, differing from traditional frequentist modeling.
  • The methodology is validated by comparing results with Bayesian network meta-analysis using anesthetic mortality and thrombolytic drug databases.

Main Results:

  • The multilevel network meta-analysis approach effectively accounts for publication bias and inconsistency.
  • The proposed frequentist method demonstrates flexibility, allowing for additional levels and multiple outcomes.
  • Comparisons with Bayesian network meta-analysis show comparable results, validating the new approach.

Conclusions:

  • Multilevel network meta-analysis offers a robust and flexible frequentist alternative for evidence synthesis.
  • The approach accommodates complex data structures and potential biases inherent in meta-analyses.
  • This method is adaptable and can be implemented using widely available statistical software.