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A parametric meta-analysis.

Chang Yu1, Daniel Zelterman2

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PubMed
Summary
This summary is machine-generated.

This study introduces a new method to estimate effect sizes from independent p-values in meta-analyses. The approach effectively combines multiple studies, even with varying data, to provide a reliable standardized mean difference (SMD).

Keywords:
Bayesian estimatorciticolinedistribution of p-valuesstandardized mean difference

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Area of Science:

  • Biostatistics
  • Statistical Methods
  • Meta-Analysis

Background:

  • Meta-analysis combines results from independent studies.
  • Fisher's method tests for uniform distribution of p-values.
  • Combining p-values after rejecting uniformity requires new methods.

Purpose of the Study:

  • To derive a distribution for independent, non-identically distributed p-values.
  • To estimate the standardized mean difference (SMD) using maximum likelihood estimation (MLE).
  • To provide a method for combining p-values in meta-analysis.

Main Methods:

  • Derived a p-value distribution parameterized by SMD and sample size.
  • Utilized maximum likelihood estimation (MLE) to find the SMD.
  • Conducted simulation studies to validate the method.
  • Presented a Bayes estimator for SMD and a method for publication bias.

Main Results:

  • The derived distribution includes the uniform distribution as a special case.
  • MLE of SMD provides a weighted average of effect sizes with shrinkage.
  • The method accurately estimates effect size with as few as 6 p-values.
  • Demonstrated application on meta-analyses for citicoline in memory disorders and stroke recovery.

Conclusions:

  • The proposed method effectively estimates standardized mean difference from independent p-values.
  • The approach is broadly applicable within the maximum likelihood framework.
  • The methods offer robust tools for meta-analysis, including addressing publication bias.