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Researchers suggest using a suboptimal estimator for partial correlation coefficients (PCCs) in meta-analyses. However, this study finds other estimators may perform better, indicating more research is needed for best practices.

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Area of Science:

  • Statistics
  • Biostatistics
  • Meta-analysis

Background:

  • A recent study recommended using a suboptimal estimator for partial correlation coefficients (PCCs) standard error in meta-analysis weighting.
  • This recommendation was based on Monte Carlo simulations.

Approach:

  • This study re-evaluates the simulation framework used in the prior research.
  • It explores alternative estimators for PCC standard error beyond the one recommended.
  • The performance of different estimators was compared within a simulation environment.

Key Points:

  • The previously recommended suboptimal estimator for PCC standard error is not necessarily the best performing.
  • Other estimators demonstrated superior performance in the simulations conducted.
  • The findings challenge the universality of the prior recommendation.

Conclusions:

  • The current evidence is insufficient to establish definitive best practices for meta-analyses using PCCs.
  • Further research is required to identify optimal estimators for PCC standard error in various meta-analytic contexts.
  • A cautious approach is advised for meta-analysts regarding weighting procedures with PCCs.