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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Adjusting for collider bias in genetic association studies using instrumental variable methods.

Siyang Cai1, April Hartley2, Osama Mahmoud3

  • 1Department of Health Sciences, University of Leicester, Leicester, UK.

Genetic Epidemiology
|May 18, 2022
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Summary

Genetic instrumental variable methods can address collider bias in observational studies, particularly for disease progression and survival. A corrected weighted least-squares method helps reduce weak instrument bias in these analyses.

Keywords:
Mendelian randomisationascertainment biasindex event biasselection bias

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Area of Science:

  • Epidemiology
  • Genetics
  • Biostatistics

Background:

  • Genome-wide association studies (GWAS) identify genetic markers for instrumental variables in epidemiological research.
  • Collider bias, arising from conditioning on a variable associated with the outcome, affects observational studies, especially those on disease progression and survival.
  • Established Mendelian randomization (MR) methods account for confounding, but their link to genetic instrumental variable approaches for collider bias needs clarification.

Purpose of the Study:

  • To clarify the relationship between genetic instrumental variable methods and Mendelian randomization for addressing collider bias.
  • To highlight the impact of weak instrument bias in these contexts.
  • To present a corrected weighted least-squares procedure to mitigate weak instrument bias.

Main Methods:

  • Clarification of links between genetic instrumental variable methods and Mendelian randomization.
  • Identification and discussion of weak instrument bias.
  • Application of a corrected weighted least-squares procedure.
  • Illustration using two data examples: waist-hip ratio adjusted for body-mass index, and smoking cessation.

Main Results:

  • Little effect of collider bias was observed on primary association results in both data examples.
  • The corrected weighted least-squares procedure offers a simple approach to reduce weak instrument bias.
  • Potential for collider bias to propagate into substantial effects on downstream analyses, including polygenic risk scoring and Mendelian randomization.

Conclusions:

  • Genetic instrumental variable methods and Mendelian randomization share principles applicable to bias correction in observational studies.
  • Weak instrument bias is a critical consideration, and the proposed method offers a practical solution.
  • While primary associations may show minimal collider bias impact, further analyses can be significantly affected, necessitating careful application of these methods.