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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Published on: July 3, 2020

Bayesian effect estimation accounting for adjustment uncertainty.

Chi Wang¹, Giovanni Parmigiani, Francesca Dominici

¹Markey Cancer Center, University of Kentucky, Lexington, Kentucky 40536, USA. chi.wang@uky.edu

|February 28, 2012

Summary

This summary is machine-generated.

Bayesian adjustment for confounding (BAC) improves exposure effect estimation by accounting for uncertainty in selecting confounders. This novel method shows reduced bias and better coverage compared to traditional Bayesian model averaging (BMA).

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Published on: January 9, 2016

Area of Science:

Biostatistics
Epidemiology
Statistical Modeling

Background:

Model-based estimation of exposure effects is sensitive to confounder selection.
Uncertainty in choosing confounders can lead to biased results.
Existing methods may not adequately address confounding uncertainty.

Purpose of the Study:

To introduce Bayesian adjustment for confounding (BAC), a new method for estimating exposure effects.
To account for uncertainty in the selection of confounding factors.
To compare BAC with traditional Bayesian model averaging (BMA) and another recent approach.

Main Methods:

Specifying two models: an outcome model and an exposure model.
Employing Bayesian variable selection on both models.
Introducing a dependence parameter (ω) to link the models, with BAC reducing to BMA when ω=1.

Main Results:

Simulation studies demonstrate that BAC (with ω > 1) yields less biased exposure effect estimates than BMA.
BAC shows improved coverage probabilities compared to traditional BMA.
Application to air pollution and cardiovascular disease data highlights potential pitfalls of misusing variable selection.

Conclusions:

BAC offers a robust approach to handling confounding uncertainty in exposure-outcome studies.
The method provides more reliable estimates than traditional BMA, especially when confounding is uncertain.
Careful consideration of variable selection methods is crucial in epidemiological research.