Invited commentary: a critical look at some popular meta-analytic methods

Area of Science:

• Biostatistics
• Epidemiology
• Medical Research Methodology

Background:

• Meta-analysis is crucial for synthesizing research findings and identifying patterns.
• Effective meta-analysis requires rigorous identification and delineation of heterogeneity sources.
• Current common techniques may not always meet these objectives, potentially obscuring important data patterns and introducing bias.

Purpose of the Study:

• To critically evaluate common meta-analysis techniques regarding their objectivity and effectiveness in handling heterogeneity.
• To propose improvements for meta-analysis practices to enhance reproducibility and minimize bias.

Main Methods:

• Analysis of common meta-analysis techniques, including scatterplot interpretation, random-effects summaries, and quality scoring.
• Critique of subjective versus objective components in data summarization and pattern recognition.
• Proposal for replacing quality scoring with meta-regression on quality components.

Main Results:

• Scatterplot interpretations can be subjective, not objective data properties.
• Random-effects summaries may obscure important patterns, divert attention from heterogeneity, and amplify publication bias.
• Quality scoring introduces subjective bias, wastes information, and hinders heterogeneity identification.

Conclusions:

• Meta-analysis practices need refinement to ensure objective summarization and thorough heterogeneity assessment.
• Random-effects summaries should be used cautiously, only after exhaustive searches for heterogeneity sources.
• Meta-regression on quality items is a superior alternative to quality scoring for identifying heterogeneity.