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Model-based estimation of the attributable risk in case-control and cohort studies
1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, E7642, MD 21205, USA. ccox@jhsph.edu
Statistical Methods in Medical Research
|January 31, 2007
Summary
This study unifies model-based methods for estimating adjusted attributable risk (AR) in case-control and cohort studies. It presents practical computational approaches using standard statistical software for improved confounding adjustment in epidemiological research.
Area of Science:
- Epidemiology
- Biostatistics
- Statistical Modeling
Background:
- Attributable risk (AR) estimation is crucial for public health.
- Existing model-based methods for adjusted AR require distinct approaches for case-control and cohort studies.
- Confounding factors necessitate robust adjustment methods in risk estimation.
Purpose of the Study:
- To provide a comprehensive review and illustration of model-based methods for adjusted attributable risk estimation.
- To unify and simplify model-based approaches for both case-control and cohort study designs.
- To discuss practical computation of standard errors and generalized AR estimation.
Main Methods:
- Review and comparison of existing model-based estimators for attributable risk.
- Development of a unified approach for case-control studies using logistic regression.
- Proposal of a loglinear model for cohort studies with cross-sectional or stratified sampling.
- Application of the delta method and bootstrap for standard error estimation.
Main Results:
- Two previously proposed logistic regression approaches for case-control studies are shown to be identical, enabling a unified method.
- A practical loglinear model approach is presented for cohort studies, accommodating prevalence estimation.
- Standard errors for adjusted AR are reliably estimated using the delta method or bootstrap.
- Computations are feasible with standard statistical software even for complex models.
Conclusions:
- A unified, practical framework for model-based adjusted attributable risk estimation is established for both case-control and cohort studies.
- The proposed methods enhance the ability to adjust for confounding factors in epidemiological risk assessment.
- Standard statistical software facilitates the implementation of these advanced estimation techniques.
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