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Statistical foundations for model-based adjustments.

Sander Greenland1, Neil Pearce

  • 1Department of Epidemiology and Department of Statistics, University of California, Los Angeles, California 90095-1772;

Annual Review of Public Health
|March 19, 2015
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Summary
This summary is machine-generated.

Accurate effect estimation in epidemiology requires careful model selection, considering study context and prior information. Refined strategies focus on predicting exposure or outcomes for better confounding adjustment in causal inference.

Keywords:
causal inferenceconfoundingmodelingvariable selection

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical Modeling

Background:

  • Epidemiology textbooks often lack detail on model selection strategies.
  • Model selection is influenced by study-specific contextual information and prior knowledge of covariates.
  • Accurate effect estimation requires clear documentation of modeling goals and interpretation of parameters.

Purpose of the Study:

  • To review established covariate selection strategies in epidemiological modeling.
  • To identify shortcomings of current methods and propose refinements.
  • To emphasize the importance of context-specific model selection for accurate effect estimation.

Main Methods:

  • Review of established model covariate selection strategies.
  • Analysis of shortcomings in existing methods.
  • Proposal of refined strategies for accurate effect estimation.
  • Discussion of the shift in focus towards prediction for confounding adjustment.

Main Results:

  • Established covariate selection strategies have limitations.
  • Refined strategies can improve the accuracy of effect estimates.
  • Predictive modeling approaches can be adapted for causal inference.

Conclusions:

  • Contextual information and clear goals are crucial for epidemiological model selection.
  • Shifting focus to prediction of exposure or potential outcomes aids confounding adjustment.
  • Methods for passive prediction can be leveraged for causal inference with appropriate parameter targeting.