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Meta-analyzing partial correlation coefficients using Fisher's z transformation.

Robbie C M van Aert1

  • 1Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands.

Research Synthesis Methods
|July 8, 2023
PubMed
Summary
This summary is machine-generated.

Meta-analyzing partial correlation coefficients (PCCs) can be improved using Fisher's z transformation. This method addresses violated assumptions in standard meta-analysis models, leading to more robust and accurate results for PCC synthesis.

Keywords:
Fisher's z transformationmeta-analysispartial correlation coefficientsampling variance

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Area of Science:

  • Statistics
  • Meta-analysis
  • Correlation analysis

Background:

  • Partial correlation coefficients (PCCs) measure relationships between variables, controlling for others.
  • Standard meta-analysis models for PCCs violate assumptions regarding known sampling variance and normal distribution.

Purpose of the Study:

  • To propose and evaluate the Fisher's z transformation for meta-analyzing PCCs.
  • To address limitations of traditional meta-analysis methods for PCCs.

Main Methods:

  • Applied Fisher's z transformation to PCCs, similar to its use with Pearson correlation coefficients.
  • Reproduced a simulation study and conducted meta-analyses using both standard PCCs and Fisher's z transformed PCCs.

Main Results:

  • Fisher's z transformation for PCCs results in a sampling variance independent of the PCC value.
  • The sampling distribution of transformed PCCs more closely approximates a normal distribution.
  • Meta-analyses using Fisher's z transformed PCCs demonstrated lower bias and root mean square error compared to standard PCC meta-analyses.

Conclusions:

  • Meta-analyzing Fisher's z transformed PCCs is a viable and recommended alternative to standard PCC meta-analysis.
  • Using both methods in parallel can help assess the robustness of meta-analytic findings for PCCs.