Standardizing effect size from linear regression models with log-transformed variables for meta-analysis

Area of Science:

• Biostatistics
• Epidemiology
• Medical Research

Background:

• Meta-analysis synthesizes treatment or risk factor effects for diseases.
• Studies often use log-transformed variables for linear regression assumptions.
• Inconsistent variable transformations across studies necessitate result homogenization.

Purpose of the Study:

• To develop a method for transforming absolute and relative changes to enable meta-analysis of studies with log-transformed variables.
• To provide a unified approach for combining heterogeneous study data.

Main Methods:

• Derived formulae to convert between absolute and relative changes.
• Applied transformation procedures to all combinations of log-transformed independent and dependent variables.
• Evaluated the method using simulations with normally and asymmetrically distributed variables.

Main Results:

• The derived formulae accurately estimated effect sizes across various scenarios and change criteria.
• The procedure is valid for homogenizing results from studies with different variable transformations.
• Caution is advised for independent variables with asymmetric distributions.

Conclusions:

• The proposed procedure effectively homogenizes effect sizes for meta-analysis, regardless of variable transformation.
• This method allows the inclusion of diverse study results, enhancing the robustness of meta-analyses.
• Application demonstrated in a meta-analysis of arsenic and manganese exposure on child neurodevelopment.