Bias-adjusted meta-analysis using the quality effects model: a Stata tutorial

Area of Science:

• Biostatistics
• Epidemiology
• Research Methodology

Background:

• Traditional meta-analysis synthesizes study effect sizes but often overlooks systematic error.
• Bias-adjusted meta-analysis models have been developed to address this limitation.
• The quality effects (QE) model specifically uses methodological quality assessments to adjust pooled estimates.

Purpose of the Study:

• To provide a step-by-step guide for implementing the quality effects (QE) model in Stata.
• To demonstrate the application of the QE model for bias-adjusted meta-analysis.
• To assist researchers in enhancing the validity of synthesized evidence.

Main Methods:

• Utilized the Stata metan package for meta-analysis.
• Applied the quality effects (QE) model for bias adjustment.
• Detailed a procedural walkthrough for researchers.

Main Results:

• The QE model offers a structured approach to adjust meta-analytic estimates based on study quality.
• The Stata implementation facilitates practical application of bias-adjusted meta-analysis.
• Researchers can improve the accuracy of pooled effect sizes by accounting for methodological quality.

Conclusions:

• The quality effects (QE) model is a valuable tool for conducting bias-adjusted meta-analyses.
• Implementing the QE model in Stata enhances the rigor of evidence synthesis.
• This approach helps mitigate bias and improve the reliability of meta-analytic findings.