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Area of Science:

  • Biostatistics
  • Clinical Epidemiology

Background:

  • Foundational features of meta-analysis are crucial for reliable evidence synthesis.
  • The DerSimonian and Laird (D&L) random effects model is widely used but its performance characteristics require further investigation.

Purpose of the Study:

  • To evaluate the bias, coverage, and asymptotic behavior of the D&L meta-analysis method using simulations.
  • To assess performance across varying trial numbers, sizes, risk levels, and treatment effect extents.

Main Methods:

  • Simulated data from randomized controlled trials were used to model risk of untoward events.
  • Treatment effect was quantified as relative risk reduction, with effect size estimated by odds ratio.
  • Performance metrics included bias, standardized bias, and coverage, compared against prespecified thresholds.

Main Results:

  • Bias, standardized bias, and coverage varied significantly with trial characteristics and risk distributions.
  • Increasing trial size and number improved performance, but satisfactory results were not consistently achieved.
  • Performance was poorer with normal risk distributions compared to constant or narrow uniform distributions. Asymptotic behavior did not demonstrate bias approaching zero.

Conclusions:

  • The D&L random effects meta-analysis method demonstrated modest performance at best.
  • Asymptotic normality could not be demonstrated, raising questions about the method's validity.
  • Findings suggest caution against generic use of the D&L method, warranting replication and extension.