Cross-Cohort Mixture Analysis: A Data Integration Approach With Applications on Gestational Age and DNA-Methylation-Derived Gestational Age Acceleration Metrics.

ABSTRACT: Data integration of multiple studies can provide enhanced exposure contrast and statistical power to examine associations between environmental exposure mixtures and health outcomes. Extant research has combined populations and identified an overall mixture-outcome association, without accounting for differences across studies. We extended the Bayesian Weighted Quantile Sum (BWQS) regression to a hierarchical framework to analyze mixtures across cohorts. The hierarchical BWQS (HBWQS) approach aggregates sample size of multiple cohorts to calculate an overall mixture index, thereby identifying the most harmful exposure(s) across cohorts; and provides cohort-specific associations between the overall mixture index and the outcome. We showed results from 10 simulated scenarios including four m

SUBMITTER: Colicino E 

PROVIDER: S-EPMC12852946 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature