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Evaluating network meta-analysis and inconsistency using arm-parameterized model in structural equation modeling.

Ming-Chieh Shih1, Yu-Kang Tu1

  • 1Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.

Research Synthesis Methods
|March 6, 2019
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Summary
This summary is machine-generated.

This study introduces an arm-parameterized model for network meta-analysis (NMA) within structural equation modeling (SEM). This novel approach enhances direct-indirect evidence evaluation and unifies inconsistency assessment in NMA.

Keywords:
inconsistencymixed treatment comparisonsmultivariate analysisnetwork meta-analysisstructural equation modeling

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Network meta-analysis (NMA) integrates direct and indirect evidence for treatment comparisons.
  • Structural equation modeling (SEM) is a versatile statistical framework for analyzing complex relationships.
  • Existing NMA models in SEM, like the Lu-Ades model, use treatment contrasts, introducing correlations.

Purpose of the Study:

  • To demonstrate NMA in SEM using an arm-parameterized model.
  • To evaluate direct-indirect evidence inconsistency within this framework.
  • To extend SEM-based NMA to include within-person designs and novel meta-analysis approaches.

Main Methods:

  • Developed an arm-parameterized model for NMA within the SEM framework.
  • Utilized multiple group analysis in SEM for direct-indirect evidence inconsistency evaluation.
  • Proposed a novel arm-parameterized inconsistency model.

Main Results:

  • The proposed arm-parameterized model yields results consistent with the Lu-Ades model.
  • The framework accommodates within-person study designs and unrestricted weighted least squares.
  • The arm-parameterized inconsistency model unifies existing inconsistency evaluation methods.

Conclusions:

  • The arm-parameterized SEM approach provides a flexible and unified framework for NMA.
  • This method enhances the evaluation of direct-indirect evidence and treatment inconsistency.
  • Relaxing variance assumptions can improve statistical power in NMA.