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Bayesian hierarchical dose-response meta-analysis of epidemiological studies: Modeling and target population
Bruce Allen1, Kan Shao2, Kevin Hobbie3
1Independent Consultant, Chapel Hill, NC, USA.
Environment International
|September 24, 2020
Summary
This study introduces a hierarchical Bayesian meta-analysis for chemical risk assessment using multiple epidemiological studies. The method effectively models dose-response relationships and extrapolates cancer risks, as demonstrated with inorganic arsenic and bladder cancer.
Area of Science:
- Environmental Epidemiology
- Biostatistics
- Toxicology
Background:
- Traditional dose-response analyses face challenges with numerous high-quality epidemiological studies.
- Selecting a single "best" study can ignore valuable data, necessitating systematic approaches.
- Meta-analysis and meta-regression are established methods for integrating multi-study data on environmental contaminant risks.
Purpose of the Study:
- To propose and detail a hierarchical, Bayesian meta-analysis approach for dose-response analysis of multiple epidemiological studies.
- To focus on dose-response modeling and risk extrapolation using a flexible logistic model within a hierarchical Bayesian framework.
- To illustrate the approach using the association between bladder cancer and oral inorganic arsenic (iAs) exposure.
Main Methods:
- Employed a hierarchical Bayesian meta-analysis framework to model dose-response relationships from multiple epidemiological studies.
- Utilized a flexible logistic model capable of estimating both study-specific and pooled slopes, akin to a random effects model.
- Incorporated lifetable analysis for extrapolating estimated risks to a target population (general US population) using background exposure data.
Main Results:
- The hierarchical Bayesian approach effectively models dose-response relationships from diverse epidemiological study designs (case-control, cohort).
- The framework allows for estimation of study-specific slopes and a common pooled slope, accommodating heterogeneity.
- Risk extrapolation to the general US population was performed using bladder cancer and inorganic arsenic data.
Conclusions:
- The proposed hierarchical Bayesian meta-analysis offers a robust method for analyzing multiple epidemiological studies in environmental chemical risk assessment.
- This approach provides a systematic way to incorporate diverse data, estimate dose-response relationships, and extrapolate risks.
- The methods are generalizable for investigating associations between various pollutants and health outcomes in epidemiological research.
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