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Related Experiment Video

Updated: May 22, 2026

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Published on: November 27, 2019

An empirical study using permutation-based resampling in meta-regression.

Joel J Gagnier1, David Moher, Heather Boon

  • 1Department of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI, USA. jgagnier@umich.edu

Systematic Reviews
|May 17, 2012
PubMed
Summary
This summary is machine-generated.

Permutation methods in meta-regression with few trials did not alter final models but increased P values. This approach may reduce spurious findings in small sample meta-analyses.

Related Experiment Videos

Last Updated: May 22, 2026

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Published on: November 27, 2019

Area of Science:

  • Biostatistics
  • Evidence Synthesis

Background:

  • Meta-regression with few trials increases false positive/negative risks.
  • Normality assumptions may fail in small samples, leading to unreliable results.
  • Permutation methods offer an alternative for calculating P values to reduce spurious findings.

Purpose of the Study:

  • To empirically investigate differences between standard and permutation-based meta-regression methods.
  • To assess the impact of permutation methods on P values and model selection in small sample meta-analyses.

Main Methods:

  • Selected randomized controlled trials (RCTs) with a small number of trials (herbal medicine).
  • Performed meta-analyses and meta-regression using both large sample and permutation methods.
  • Compared final models and P values derived from each method.

Main Results:

  • Covariates in final models were identical across both methods in all tested cases.
  • Permutation methods yielded larger P values in 78% of cases.
  • P values were identical between methods in 22% of cases.

Conclusions:

  • Permutation-based resampling may not alter final models in backwards stepwise regression for meta-regression.
  • Permutation methods may increase P values in meta-regression with multiple covariates and small sample sizes.
  • This suggests permutation methods could offer a more conservative approach to identifying significant covariates in meta-regression.