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A forward search algorithm for detecting extreme study effects in network meta-analysis.

Maria Petropoulou1,2, Georgia Salanti3, Gerta Rücker1

  • 1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.

Statistics in Medicine
|July 22, 2021
PubMed
Summary
This summary is machine-generated.

Identifying outliers in network meta-analysis (NMA) is crucial. The forward search (FS) algorithm effectively detects studies causing heterogeneity and inconsistency in NMA, improving results.

Keywords:
Cook's distanceNMAoutlierforward searchnetwork meta-analysisoutliers

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Evidence Synthesis

Background:

  • Meta-analysis and network meta-analysis (NMA) synthesize evidence from multiple studies.
  • Extreme study effects (outliers) can distort summary estimates and inflate heterogeneity.
  • Detecting outliers is challenging, especially in the complex multivariate data of NMA.

Purpose of the Study:

  • To extend the forward search (FS) algorithm for outlier detection in network meta-analysis (NMA).
  • To address the challenges of visualizing outliers in multivariate NMA data.
  • To identify studies contributing to both heterogeneity and inconsistency in NMA.

Main Methods:

  • Applied the forward search (FS) algorithm, a statistical method for identifying outliers.
  • Extended the FS algorithm to the network meta-analysis (NMA) framework.
  • Utilized real and artificial network data containing outliers for validation.
  • Developed an R package (NMAoutlier) for method replication and dissemination.

Main Results:

  • The extended FS algorithm successfully identified outlier studies in network meta-analysis (NMA).
  • Demonstrated the algorithm's utility in detecting studies contributing to heterogeneity and inconsistency.
  • The NMAoutlier R package facilitates the application and validation of the proposed method.

Conclusions:

  • The forward search (FS) algorithm is an effective visual diagnostic tool for outlier detection in network meta-analysis (NMA).
  • This method aids in identifying studies that potentially compromise NMA results by introducing heterogeneity and inconsistency.
  • The developed R package supports the practical implementation and reproducibility of outlier detection in NMA.