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A REML method for the evidence-splitting model in network meta-analysis.

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This study introduces a new restricted maximum likelihood (REML) method for network meta-analysis, improving upon the evidence-splitting (ES) model by reducing bias in heterogeneity variance estimates.

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Epidemiology

Background:

  • Network meta-analysis integrates diverse evidence, but assessing direct vs. indirect evidence inconsistency is crucial.
  • The evidence-splitting (ES) model separates direct and indirect evidence but uses full maximum likelihood (ML), potentially causing biased heterogeneity variance estimates.

Purpose of the Study:

  • To propose and evaluate a restricted maximum likelihood (REML) method for the evidence-splitting (ES) model in network meta-analysis.
  • To address the known bias in variance component estimation associated with full ML in mixed-effects models.

Main Methods:

  • Developed a REML-based estimation method for the ES model parameters.
  • Conducted simulations to compare the performance of the REML method against the full ML approach.
  • Analyzed bias and mean squared error of parameter estimates.

Main Results:

  • The proposed REML method demonstrated competitive performance compared to full ML methods.
  • REML showed comparable bias and mean squared error in simulations.
  • Identified limitations of the ES model, noting it models inconsistency but doesn't explain its cause.

Conclusions:

  • The REML approach offers a viable alternative for estimating parameters in the ES model, mitigating bias issues.
  • While effective for separating evidence, the ES model's utility in explaining inconsistency requires further consideration.