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Nonparametric Bayesian functional selection in 1-M matched case-crossover studies
Wenyu Gao1, Inyoung Kim1, Eun Sug Park2
1Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
Statistical Methods in Medical Research
|October 21, 2022
Summary
This study introduces a novel Bayesian approach for case-crossover studies, enabling automatic variable and function selection while accounting for effect modifications. This method enhances the analysis of disease risk and binary outcomes in public health and epidemiology.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- Matched case-crossover studies are widely used in public health, biomedical, and epidemiological research for analyzing clustered binary outcomes.
- Traditional methods struggle with variable selection and simultaneous adjustment for effect modifications when numerous covariates are present.
- Existing semiparametric models offer limited options for automatic variable and functional selection alongside effect modification evaluation.
Purpose of the Study:
- To propose a unified Bayesian approach for matched case-crossover studies.
- To enable simultaneous variable and functional selection.
- To account for effect modifications by matching covariates, such as time, in the analysis of disease risk.
Main Methods:
- A unified Bayesian framework is developed to detect both parametric and nonparametric relationships.
- The approach facilitates automatic variable and functional selection.
- It accounts for potential effect modifications by matching covariates, including time.
Main Results:
- The proposed Bayesian method effectively performs automatic variable and functional selection.
- It successfully accounts for effect modifications by matching covariates.
- Demonstrated advantages through simulation studies and a real-world epidemiological example.
Conclusions:
- The unified Bayesian approach offers a powerful tool for analyzing matched case-crossover data.
- It addresses limitations in existing methods for variable selection and effect modification analysis.
- This approach enhances the understanding of disease risk and binary outcomes in epidemiological research.
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