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Combining the evidence using stable weights.

Elena Kulinskaya1, Stephan Morgenthaler2, Robert G Staudte3

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Comparing meta-analysis methods for risk difference, this study found the inverse-variance weights approach unreliable. For equal risks, Cochran

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Meta-analysis combines study results to enhance decision accuracy.
  • Accurate combination methods are crucial for reliable risk difference estimation.

Purpose of the Study:

  • To compare four methods for combining risk difference estimates: Cochran, Mantel-Haenszel (MH), inverse-variance weights, and variance-stabilized weights.
  • To evaluate the performance of these methods across various sample sizes and parameter configurations.

Main Methods:

  • Simulation study comparing statistical methods for meta-analysis.
  • Evaluation metrics included test statistic level and power, and confidence interval coverage.
  • Analysis covered scenarios with equal and unequal risks.

Main Results:

  • The inverse-variance weights method is unreliable and not recommended.
  • For equal risks, the Cochran test and confidence intervals demonstrated the highest reliability.
  • Under unequal risks, variance-stabilized confidence intervals generally outperformed others, except for small average risks where MH intervals were preferable.

Conclusions:

  • The choice of meta-analysis method for risk difference depends on the specific study conditions, particularly risk distribution.
  • The inverse-variance weights method should be avoided due to unreliability.
  • Variance-stabilized and MH methods offer more reliable confidence intervals in different scenarios.