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Quantifying biases in causal models: classical confounding vs collider-stratification bias.

Sander Greenland1

  • 1Department of Epidemiology, University of California Los Angeles, Los Angeles, CA 90095-1772, USA. lesdomes@ucla.edu

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Summary
This summary is machine-generated.

Stratifying analysis on variables affected by exposure can cause selection bias. This study shows collider stratification bias can be comparable to classical confounding, impacting epidemiological research.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Stratification on variables influenced by exposure can introduce selection bias.
  • Stratifying on a variable preceding exposure and disease can induce confounding, even without crude confounding.

Purpose of the Study:

  • To examine the relative magnitudes of bias from collider stratification.
  • To compare these biases with classical confounding under simple causal models.

Main Methods:

  • Graphical causal models were used to depict stratification variables as colliders.
  • Analysis focused on the relative sizes of induced biases.

Main Results:

  • Bias from stratifying on variables affected by exposure and disease can be comparable to classical confounding.
  • Other biases arising from collider stratification tend to be smaller.

Conclusions:

  • Collider stratification can introduce significant bias in epidemiological studies.
  • Understanding these biases is crucial for accurate causal inference and study design.