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Related Experiment Videos

Collider scope: when selection bias can substantially influence observed associations.

Marcus R Munafò1,2, Kate Tilling1,3, Amy E Taylor1,2

  • 1MRC Integrative Epidemiology Unit.

International Journal of Epidemiology
|October 18, 2017
PubMed
Summary
This summary is machine-generated.

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Selection bias in health studies, including genetic and environmental factors, can distort association estimates. Understanding and adjusting for selection factors is crucial for accurate research findings.

Area of Science:

  • Epidemiology
  • Genetics
  • Biostatistics

Background:

  • Large-scale health studies rely on cross-sectional and cohort data to understand genetic and environmental influences.
  • Sample representativeness in these studies can be compromised by selection into the study or attrition over time.

Purpose of the Study:

  • To investigate the impact of selection bias on association estimates in health studies.
  • To demonstrate how selection bias, viewed as collider bias, can distort findings.

Main Methods:

  • Exploration of selection bias as conditioning on a collider.
  • Simulations to assess the effect of selection on phenotypic and genotypic associations.

Main Results:

  • Selection bias can lead to substantially biased estimates of associations, contrary to common assumptions.

Related Experiment Videos

  • Even modest selection influences can generate misleading phenotypic and genotypic association estimates.
  • Selection related to phenotypes can particularly bias associations with relevant genetic variants.

    • Conclusions:

    • Selection bias poses a significant threat to the validity of association estimates in health research.
    • Identifying and adjusting for factors influencing study selection and attrition is essential.
    • Methods like analyzing polygenic score prediction of participation can enable sensitivity analyses for bias.