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A generalized-weights solution to sample overlap in meta-analysis.

Pedro R D Bom1, Heiko Rachinger2

  • 1Deusto Business School, University of Deusto, Bilbao, Spain.

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Summary
This summary is machine-generated.

Meta-analyses using overlapping samples inflate false positive rates. A new generalized-weights (GW) meta-estimator corrects this by modeling sample dependence, restoring statistical accuracy and improving efficiency.

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Area of Science:

  • Statistical methodology
  • Meta-analysis
  • Econometrics

Background:

  • Meta-studies frequently utilize empirical findings from overlapping samples, particularly in fields relying on aggregated observational data.
  • This sample overlap, common when multiple estimates derive from the same study or dataset, introduces dependencies that can distort statistical properties.
  • Failure to account for sample overlap in meta-analyses can lead to inflated rates of false positives, especially with large meta-sample sizes.

Purpose of the Study:

  • To analytically demonstrate how sample overlap inflates false positive rates in meta-analyses.
  • To propose a novel meta-estimator, the generalized-weights (GW) meta-estimator, to address the issue of sample overlap.
  • To validate the GW meta-estimator's performance through simulations, assessing its ability to control false positives and enhance efficiency.

Main Methods:

  • Derivation of analytical conditions under which sample overlap compromises conventional meta-estimators.
  • Development of the generalized-weights (GW) meta-estimator, which models the variance-covariance matrix of dependent estimates.
  • Construction of the variance-covariance matrix using standard sample size and overlap information from primary studies.

Main Results:

  • The GW meta-estimator effectively reduces false positive rates to their nominal levels, mitigating the inflation caused by sample overlap.
  • Monte Carlo simulations quantify significant efficiency gains of the GW meta-estimator compared to standard meta-analysis techniques.
  • The GW method is adaptable to various effect sizes beyond regression coefficients, including Cohen's d and odds ratios.

Conclusions:

  • The generalized-weights (GW) meta-estimator provides a robust solution for meta-analyses with overlapping samples, ensuring statistical validity.
  • This method corrects for the correlation structure induced by sample overlap, leading to more reliable research syntheses.
  • The GW approach is practical, requiring commonly available data, and offers improved statistical power in meta-analytic research.