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Network meta-analysis with integrated nested Laplace approximations.

Rafael Sauter1, Leonhard Held1

  • 1Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001 Zürich, Switzerland.

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|September 12, 2015

View abstract on PubMed

Summary
This summary is machine-generated.

Integrated Nested Laplace Approximation (INLA) offers a faster Bayesian inference method for network meta-analysis (NMA) compared to Markov Chain Monte Carlo (MCMC). This approach maintains accuracy while significantly reducing computation time for NMA models.

Keywords:
Bayesian inferenceIntegrated nested Laplace approximationsNetwork meta-analysisNode-splitting

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Area of Science:

  • Statistical modeling
  • Biostatistics
  • Evidence synthesis

Background:

  • Network meta-analysis (NMA) is increasingly used for evidence synthesis and decision-making.
  • Bayesian inference is common for NMA, especially with correlated random effects in multiarm trials.
  • Markov Chain Monte Carlo (MCMC) sampling is the standard Bayesian method but is computationally intensive.

Purpose of the Study:

  • To demonstrate the application of Integrated Nested Laplace Approximations (INLA) for Bayesian inference in NMA models.
  • To present INLA as a computationally efficient alternative to MCMC for NMA.
  • To illustrate INLA's utility for various NMA aspects, including multiarm trials and inconsistency assessment.

Main Methods:

  • Application of INLA to NMA models using both summary-level and trial-arm level data.
  • Modeling of multiarm trials and inference for functional contrasts using INLA.
  • Utilizing node-splitting within INLA for assessing network inconsistency.

    • Main Results:

    • INLA provides a computationally efficient alternative to MCMC for NMA.
    • INLA can be applied to various NMA data structures, including multiarm trials.
    • INLA facilitates the assessment of network inconsistency.

    Conclusions:

    • INLA is a viable and efficient method for Bayesian inference in network meta-analysis.
    • The approach offers substantial computational savings without compromising accuracy.
    • INLA enhances the practical application of NMA for evidence synthesis and decision-making.