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Meta-analysis in Stata using gllamm.

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Summary
This summary is machine-generated.

This study demonstrates how the versatile gllamm software in Stata can perform advanced meta-analysis, including handling correlated estimates and hierarchical data, overcoming limitations of existing Stata programs.

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Area of Science:

  • Statistics
  • Biostatistics
  • Quantitative Research Methods

Background:

  • Existing Stata meta-analysis programs (metan, metareg, mvmeta, glst) have limitations.
  • Specific meta-analysis types like univariate with correlated estimates, multilevel, or longitudinal data lack dedicated software.
  • A unified approach for diverse meta-analysis models in Stata is needed.

Purpose of the Study:

  • To demonstrate the utility of the gllamm software for fitting various complex meta-analysis models in Stata.
  • To provide practical applications and instructions for using gllamm to overcome limitations of existing Stata meta-analysis tools.
  • To present a general exposition of fitting diverse meta-analysis models as special cases of a general linear mixed-model formulation using gllamm.

Main Methods:

  • Utilizing the gllamm software in Stata for meta-analysis.
  • Employing transformations based on Cholesky decomposition of the inverse covariance matrix.
  • Applying generalized least squares (GLS) methods to handle correlated data within meta-analysis models.

Main Results:

  • Gllamm can successfully fit a wide range of disparate meta-analysis models, including those with correlated estimates, multilevel structures, and longitudinal data.
  • The proposed method effectively handles correlated data through GLS and appropriate matrix transformations.
  • The versatility of gllamm allows its application across various disciplines for complex meta-analysis.

Conclusions:

  • Gllamm offers a powerful and flexible solution for advanced meta-analysis in Stata, addressing limitations of existing software.
  • The generalized linear mixed-model framework implemented in gllamm provides a unified approach for diverse meta-analysis scenarios.
  • Researchers can leverage gllamm for sophisticated meta-analysis, including handling correlated and hierarchical data, with practical guidance provided.