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Combining estimators in interlaboratory studies and meta-analyses.

Hening Huang1

  • 1Teledyne RD Instruments (retired), 14020 Stowe Drive, Poway, California, 92064, USA.

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|March 14, 2023
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Summary

This study introduces an estimator-averaging approach for statistical analysis in meta-analyses and interlaboratory studies. This method combines multiple estimators to improve accuracy and account for estimator uncertainty, enhancing study conclusions.

Keywords:
consensus valueheterogeneityinterlaboratory studymeta-analysisuncertainty

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Area of Science:

  • Statistical methodology
  • Meta-analysis
  • Interlaboratory studies

Background:

  • Numerous statistical estimators exist for consensus value and heterogeneity variance in meta-analyses and interlaboratory studies.
  • Each estimator has inherent uncertainty, leading to significant variations in results for the same dataset.
  • The choice of estimator can critically impact study conclusions, yet no universal metric exists for optimal selection.

Approach:

  • Proposes an estimator-averaging method to synthesize results from multiple individual estimators.
  • The averaged estimator is a linear combination of individual estimators, incorporating three sources of uncertainty.
  • Evaluates performance using Monte Carlo simulations and a case study on the Newtonian constant of gravitation.

Key Points:

  • Individual estimators for consensus value and heterogeneity variance can yield divergent results due to estimator uncertainty.
  • The proposed estimator-averaging approach offers a robust alternative to selecting a single, potentially suboptimal, estimator.
  • The method accounts for multiple sources of uncertainty, providing a more reliable estimate.

Conclusions:

  • The estimator-averaging approach provides a more stable and reliable estimation of consensus values and heterogeneity variance.
  • This method mitigates the impact of individual estimator limitations and uncertainty.
  • Applicable to diverse scientific fields, including physics, by combining various statistical and Bayesian methods.