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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Model-Based Estimation of the Attributable Risk: A Loglinear Approach.

Christopher Cox1, Xiuhong Li

  • 1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health.

Computational Statistics & Data Analysis
|October 11, 2012
PubMed
Summary
This summary is machine-generated.

This study proposes using the Poisson model for estimating adjusted attributable risk (AR) in both case-control and cohort studies. This approach provides general variance expressions and facilitates calculation of standard errors and confidence limits.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical Modeling

Background:

  • Previous methods for estimating adjusted attributable risk (AR) primarily used logistic regression for case-control studies and Poisson models for cohort studies.
  • An earlier review highlighted these distinct approaches, noting the need to account for variability in exposure and covariate distributions.

Purpose of the Study:

  • To present a unified model-based approach for estimating adjusted attributable risk (AR) in both case-control and cohort studies.
  • To introduce general expressions for the asymptotic variance of AR using the Poisson model and the delta method.
  • To extend the concept of AR to a generalized attributable risk (gAR) for situations where exposure is not fully eliminated.

Main Methods:

  • Application of the Poisson regression model to both case-control and cohort studies for AR estimation.
  • Utilizing the delta method to derive general expressions for the asymptotic variance of AR.
  • Employing bootstrap resampling for computing standard errors and confidence limits, enabling flexible binary regression models for cohort studies.

Main Results:

  • The Poisson model is demonstrated as a suitable method for AR estimation in case-control studies, aligning with its use in cohort studies.
  • Generalizable variance expressions for AR and gAR are provided, adaptable to standard statistical software with Poisson regression and matrix algebra capabilities.
  • Bootstrap methods offer robust computation of standard errors and confidence intervals, enhancing flexibility in model selection for cohort data.

Conclusions:

  • The Poisson model offers a consistent and flexible framework for estimating adjusted attributable risk across different study designs.
  • The derived variance expressions and bootstrap techniques provide practical tools for researchers to accurately quantify attributable risk and its uncertainty.
  • This unified approach simplifies the analysis of attributable risk, promoting more consistent and reliable epidemiological research findings.