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Bayesian hierarchical models for network meta-analysis incorporating nonignorable missingness.

Jing Zhang1, Haitao Chu2, Hwanhee Hong3

  • 11 Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD, USA.

Statistical Methods in Medical Research
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Summary
This summary is machine-generated.

This study introduces a new network meta-analysis method to handle missing treatment data, even when missingness is not random. This improves the reliability of comparing multiple treatments in medical research.

Keywords:
Bayesian hierarchical modelsNetwork meta-analysisnonignorable missingnessselection models

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Clinical Epidemiology

Background:

  • Network meta-analysis (NMA) synthesizes evidence from multiple randomized controlled trials (RCTs) to compare numerous treatments simultaneously.
  • Standard NMA methods often struggle with sparse data matrices, common in RCTs where pairwise comparisons are prevalent.
  • Existing arm-based methods assume missing data is 'missing at random', which may not hold true in clinical trial designs.

Purpose of the Study:

  • To extend existing network meta-analysis methods to account for 'missingness not at random' (MNAR).
  • To develop and evaluate a novel selection model approach for NMA under MNAR conditions.
  • To compare the performance of the proposed MNAR method against traditional approaches.

Main Methods:

  • Developed an extended arm-based network meta-analysis method incorporating selection models to address MNAR.
  • Applied the proposed method to two real-world network meta-analysis datasets.
  • Conducted extensive simulation studies to evaluate the method's performance and robustness.

Main Results:

  • The proposed selection model approach effectively incorporates MNAR into network meta-analysis.
  • Simulations demonstrated the method's ability to provide more reliable inferences compared to methods assuming data are missing at random.
  • Comprehensive comparisons were made with contrast-based and standard arm-based methods under various missing data scenarios.

Conclusions:

  • The developed method offers a robust solution for network meta-analysis when missing treatment data is non-ignorable.
  • This advancement enhances the validity and reliability of synthesizing evidence for multiple treatment comparisons in healthcare.
  • The approach provides a valuable tool for researchers dealing with complex missing data patterns in NMA.