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Identifying inconsistency in network meta-analysis: Is the net heat plot a reliable method?

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The net heat plot, a tool for network meta-analysis inconsistency, may be misleading. It does not reliably detect or locate inconsistencies in treatment effect evidence.

Keywords:
inconsistencynet heat plotnetwork meta-analysis

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Area of Science:

  • Biostatistics
  • Evidence Synthesis
  • Health Research Methodology

Background:

  • Network meta-analysis (NMA) is challenged by inconsistency, where direct and indirect evidence conflict.
  • Inconsistency complicates the estimation and interpretation of treatment effects and contrasts.
  • The net heat plot was proposed as a graphical tool to identify and locate inconsistency in NMA.

Purpose of the Study:

  • To evaluate the reliability of the net heat plot in detecting and locating inconsistency in NMA.
  • To compare the net heat plot's performance against other established inconsistency assessment methods.
  • To investigate the theoretical underpinnings and practical implications of the net heat plot's calculations.

Main Methods:

  • Applied the net heat plot to a network meta-analysis of individual participant data for lung cancer survival.
  • Compared net heat plot findings with Bucher's approach, Cochran's Q, node-splitting, and the inconsistency parameter.
  • Conducted theoretical analysis of the net heat plot's statistical calculations.
  • Utilized a simulation study and a diabetes treatment network meta-analysis for further illustration.

Main Results:

  • The net heat plot found no significant inconsistency in the lung cancer data, contradicting other methods.
  • Theoretical analysis revealed the net heat plot's calculations involve arbitrary weighting of evidence.
  • Simulation and diabetes data analyses further demonstrated potential misleading results from the net heat plot.
  • Other methods (Bucher, Q, node-splitting, parameter) indicated inconsistency at the 5% level.

Conclusions:

  • The net heat plot is not a reliable method for signaling or identifying sources of inconsistency in network meta-analysis.
  • Its underlying calculations may arbitrarily weight evidence, leading to potentially misleading interpretations.
  • Researchers should exercise caution when using the net heat plot and rely on multiple methods for inconsistency assessment.