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Case-control studies of gene-environment interaction: Bayesian design and analysis.
Bhramar Mukherjee1, Jaeil Ahn, Stephen B Gruber
1Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA. bhramar@umich.edu
Biometrics
|November 26, 2009
Summary
This study introduces a Bayesian approach for analyzing gene-environment interactions, offering a flexible way to incorporate prior information and determine sample sizes for future studies on complex diseases.
Area of Science:
- Epidemiology
- Biostatistics
- Genetics
Background:
- Epidemiologic studies increasingly investigate gene-environment interactions.
- Prior information on candidate genes and exposures can inform study design and analysis.
- Traditional methods often assume gene-environment independence, which may not hold true.
Purpose of the Study:
- To propose a Bayesian framework for analyzing gene-environment interactions.
- To develop Bayesian criteria for sample size determination in interaction studies.
- To illustrate the application of Bayesian methods using a colorectal cancer case-control study.
Main Methods:
- Full Bayesian analysis for gene-environment interaction.
- Bayesian sample size determination for estimation and hypothesis testing.
- Elicitation of a design prior from existing data.
- Comparison of Bayesian and frequentist approaches for design and analysis.
Main Results:
- The Bayesian approach naturally incorporates uncertainty in gene-environment independence.
- Demonstrated use of existing data to inform prior distributions for future study design.
- Provided methods for sample size calculation tailored to interaction parameters.
- Illustrated application with N-acetyl transferase type 2 (NAT2) interaction in colorectal cancer.
Conclusions:
- Bayesian methods offer a robust framework for gene-environment interaction studies.
- Incorporating prior information enhances the efficiency of study design and analysis.
- The proposed methods provide valuable tools for planning future epidemiologic research.
- Bayesian strategies can complement or improve upon frequentist counterparts.
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