Background: Stratification analyses have been widely utilized in molecular association meta-analyses to estimate the interaction between genetic and environmental factors or to control for the confounding variables linked to a disease. Two calculation methods utilized in practical research, which are known as the variants of factorial stratification analysis and confounder-controlling stratification analysis in our nomenclature, have been applied in previous studies, but none of which have presented a methodology and application for these analyses.
Methods: In this paper, these two approaches are integrated and further developed into a standard procedure for stratification analysis. We first propose the advanced statistical methodology and theoretical algorithm of these three types of stratification analysis and then provide two example applications in meta-analyses of molecular association to illustrate the computing processes and interpretation of the results.
Results: The standard stratification analysis synthesizes the advantages of the first two practical methods, including identifying and controlling confounding moderators or revealing and calculating gene-environment interactions, to efficiently classify the real influence of various investigated factors on a disease in the general population. Additional challenges concerning this method and their potential solutions are also discussed, such as the approach to utilizing only the partially stratified data available in meta-research practice.
Conclusions: The standard stratification method will be extensively applicable to rapidly expanding future research on the complex relationships among genetics, environment, disease, and other variables.
Keywords: Biomarkers; Comprehensive meta-analysis; Genetic polymorphisms; Mixed-effects model; Risk factors; Stratified data.